Predictive Modeling of Aqueous Dye Removal by application of Artificial Neural Networks                 Ashutosh Tamrakar ...
Ashu Tamrakar                                                 CHE 391: Independent Study                         Advisors:...
Ashu Tamrakar                                                                        CHE 391: Independent StudyAbstractThi...
Ashu Tamrakar                                                                                                             ...
Ashu Tamrakar                                                                                                             ...
Ashu Tamrakar                                                                                                             ...
Ashu Tamrakar                                                                          CHE 391: Independent StudyChapter 1...
Ashu Tamrakar                                                                            CHE 391: Independent StudyIn orde...
Ashu Tamrakar                                                                           CHE 391: Independent Studycontact ...
Ashu Tamrakar                                                                              CHE 391: Independent Studydeter...
Ashu Tamrakar                                                                        CHE 391: Independent Studytested over...
Ashu Tamrakar                                                                         CHE 391: Independent Study(Spring 20...
Ashu Tamrakar                                                                           CHE 391: Independent StudyChapter ...
Ashu Tamrakar                                                                         CHE 391: Independent Study          ...
Ashu Tamrakar                                                                          CHE 391: Independent Study2.3      ...
Ashu Tamrakar                                                                             CHE 391: Independent Studyinclud...
Ashu Tamrakar                                                                   CHE 391: Independent Study                ...
Ashu Tamrakar                                                                          CHE 391: Independent Study2.5     A...
Ashu Tamrakar                                                                              CHE 391: Independent Studymater...
Ashu Tamrakar                                                                          CHE 391: Independent StudyA. Langmu...
Ashu Tamrakar                                                                          CHE 391: Independent StudyChapter 3...
Ashu Tamrakar                                                                          CHE 391: Independent Study Figure 2...
Ashu Tamrakar                                                                           CHE 391: Independent Study3.3     ...
Ashu Tamrakar                                                                           CHE 391: Independent StudyThere ar...
Ashu Tamrakar                                                                             CHE 391: Independent Study Figur...
Ashu Tamrakar                                                                       CHE 391: Independent Study   c. The te...
Ashu Tamrakar                                                                          CHE 391: Independent StudyChapter 4...
Ashu Tamrakar                                                                         CHE 391: Independent Study4.3     Da...
Ashu Tamrakar                                                                    CHE 391: Independent Study               ...
Ashu Tamrakar                                                                            CHE 391: Independent Study4.4    ...
Ashu Tamrakar                                                                            CHE 391: Independent StudyStep 2:...
Ashu Tamrakar                                                                                                             ...
Ashu Tamrakar                                                                                                             ...
Ashu Tamrakar                                                                         CHE 391: Independent StudyAs can be ...
Ashu Tamrakar                                                                        CHE 391: Independent StudyChapter 5. ...
Ashu Tamrakar                                                                               CHE 391: Independent Study5.2 ...
37     Figure 11. Dependence of Neural network performance on the number of neuron at hidden     layer and the distributio...
Ashu Tamrakar                                                                       CHE 391: Independent StudySince the da...
Ashu Tamrakar                                                                       CHE 391: Independent Study5.3    Prese...
Ashu Tamrakar                                                                         CHE 391: Independent StudyThere are ...
Ashu Tamrakar                                                                        CHE 391: Independent StudyChapter 6. ...
42     Figure 14. Prediction for the performance of adsorbents at different pH levels.. The circled         adsorbents are...
43     Figure 15. Prediction for the performance of adsorbents at different initial dye concentration levels.             ...
44                                                                                           CHE 391: Independent Study   ...
45                                                                                                    CHE 391: Independent...
46     Figure 18. Prediction for the performance of adsorbents at different adsorbate dosage conditions.                  ...
Ashu Tamrakar                                                                              CHE 391: Independent Study6.2  ...
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Predictive modeling of methylene blue dye removal by using artificial neural network
Upcoming SlideShare
Loading in …5
×

Predictive modeling of methylene blue dye removal by using artificial neural network

2,693 views

Published on

0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
2,693
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
0
Comments
0
Likes
2
Embeds 0
No embeds

No notes for slide

Predictive modeling of methylene blue dye removal by using artificial neural network

  1. 1. Predictive Modeling of Aqueous Dye Removal by application of Artificial Neural Networks Ashutosh Tamrakar ChE 391: Independent Study Department of Chemical and Biomolecular Engineering Lafayette College, Fall 2011-Spring 2012
  2. 2. Ashu Tamrakar CHE 391: Independent Study Advisors:Dr. Polly Piergiovanni, Chemical and Biomolecular Department Dr. Michael Senra, Chemical and Biomolecular Department Dr. Chun Wai Liew, Computer Science Department Lafayette College, Easton, PA 2
  3. 3. Ashu Tamrakar CHE 391: Independent StudyAbstractThis paper introduces dye removal processes in general and presents the fundamental and practicalaspects of neural networks techniques including an overview of their structures, strengths, andlimitations. The main objective of this study is to apply neural network modeling to predict theMethylene Blue dye adsorption onto different commercial and non-conventional adsorbents as wellas to subsequently use the network model in ranking and identifying the best choice of adsorbentsfor different wastewater conditions.This work also illustrates the development of a comprehensive adsorbent database utilizing literatureexperimental data available online and methods to prepare the data for use in neural networkmodeling. In this project, Matlab Neural Network toolbox developed by the The MathWorks, Inc.was used to develop the predictive model discussed. This research is unique in that it reports thecomprehensive modeling of dye removal by 22 different adsorbents and provides insight into aranking system which will be helpful in establishing a dye effluent treatment plant. 3
  4. 4. Ashu Tamrakar CHE 391: Independent StudyContentsChapter 1. Introduction .................................................................................................................................... 7 1.1 Introduction to Dye Pollution......................................................................................................... 7 1.2 Research Motivation ......................................................................................................................... 8 1.3 Neural Network in adsorption kinetic study ................................................................................. 9 1.4 Research Aim ...................................................................................................................................11 1.5 Paper Organization .........................................................................................................................11Chapter 2. Dyes and Dye Removal ...............................................................................................................13 2.1 Colorants and Dyes.........................................................................................................................13 2.2 Classification systems for dyes ......................................................................................................13 2.3 Methylene Blue (MB) ......................................................................................................................15 2.4 Methods of removing dye from wastewater................................................................................16 2.5 Adsorption Process .........................................................................................................................18 2.7 Common adsorption isotherms ....................................................................................................19Chapter 3. Artificial neural networks ............................................................................................................21 3.1 Introduction to ANN .....................................................................................................................21 3.2 Neural Network architecture .........................................................................................................21 3.3 Network Learning ...........................................................................................................................23 3.4 Methods for Validation of Neural Networks..............................................................................24 3.5 Advantages and disadvantages of ANN (4): ...............................................................................25Chapter 4. Database Collection .....................................................................................................................27 4.1 Variables ...........................................................................................................................................27 4.2 Adsorbents studied .........................................................................................................................27 4.3 Data Source ......................................................................................................................................28 4.4 Data Extraction Process.................................................................................................................30 4.5 Ranges of input data collected.......................................................................................................31 4.6 Data Preparation .............................................................................................................................34Chapter 5. NN modeling of adsorption .......................................................................................................35 5.1 Model Development .......................................................................................................................35 5.2 Optimization of the NN architecture ..........................................................................................36 5.3 Present Study Results......................................................................................................................39Chapter 6. NN Applications ..........................................................................................................................41 4
  5. 5. Ashu Tamrakar CHE 391: Independent Study 6.1 Comparative predictions of variable effect on removal efficiency ..........................................41 6.2 Results ...............................................................................................................................................47Works Cited .....................................................................................................................................................48List of AppendicesAppendix A – Raw Dataset for Dye Removal EfficiencyAppendix B – Matlab Fuction file for Neural Network simulationAppendix C – Detailed Adsorbent ranking 5
  6. 6. Ashu Tamrakar CHE 391: Independent StudyList of FiguresFigure 1. Molecular Structure of Methylene Blue dye (20) ........................................................................15Figure 2 Optimal ANN structure, together with a BP algorithm for the prediction of the PollutantRemoval Efficiency (PRE) (11) .....................................................................................................................22Figure 3.Training, validation and test mean squared error for the Levenberg-Marquardt algorithm .25Figure 4. Extraction process for getting data points from a graph ..........................................................30Figure 5. Range of pH in the dataset collected ..........................................................................................32Figure 6. Range of initial dye concentration in the dataset collected .......................................................32Figure 7. Range of contact time in the dataset collected............................................................................33Figure 8. Range of temperatures in the dataset collected ..........................................................................33Figure 9. Range of adsorbent dose in the dataset collected.......................................................................33Figure 10. Neural network schematic for dye adsorption model .............................................................36Figure 11. Dependence of Neural network performance on the number of neuron at hidden layerand the distribution of dataset separated for training the neural network. .............................................37Figure 12. Reproducibility check for the best NN architecture combinations......................................38Figure 13. Quality of NN predictions for the training, validation and testing dataset. The overallperformance of the neural network shows 89% accuracy between predicted values and literaturedata. ....................................................................................................................................................................39Figure 14. Prediction for the performance of adsorbents at different pH levels.. The circledadsorbents are commercial carbons while the rest are non-conventional adsorbents...........................42Figure 15. Prediction for the performance of adsorbents at different initial dye concentration levels.............................................................................................................................................................................43Figure 16. Prediction for the performance of adsorbents at different contact times. ...........................44Figure 17. Prediction for the performance of adsorbents at different temperature conditions. ..........45Figure 18. Prediction for the performance of adsorbents at different adsorbate dosage conditions. .46List of TablesTable 1. Classes of Dyes and Their Chemical Types (13) ..........................................................................14Table 2. Existing and emerging processes for dye removal (2).................................................................17Table 3. Examples of adsorbents used in wastewater treatment (2) ........................................................19Table 4. List of adsorbents and source article for the development of ANN model ............................28Table 5. Statistical Index of Input and Output Data ..................................................................................31Table 6. Ranking spectrum of each adsorbent at various variable conditions ........................................47 6
  7. 7. Ashu Tamrakar CHE 391: Independent StudyChapter 1. Introduction1.1 Introduction to Dye PollutionSynthetic dyes are widely used in textile, rubber, paper, plastic, and cosmetic industries for coloringpurposes due to their color variety, fastness, and ease of production as compared to natural dyes. (1)Since most of these industries also consume substantial volumes of water, they generate aconsiderable amount of colored aqueous waste. More than 100,000 commercially available dyes existin the market today with an annual production rate of more than 70,000 tons. (2) With such a largeproduction rate and good water solubility, these dyes are found frequently in industrial wastewater.An indication of the scale of the problem is given by the fact that approximately 2% per cent of dyesthat are produced are discharged directly in aqueous effluent (2). Furthermore, even during itsapplication, about 10 to 15% of the dye is lost from the dyeing process to the effluent (3).The effects of water contamination with these effluent dyes can be seen on two levels: effects on theenvironment and effects on humans. Even if a small amount of dye is present in water (for example,even less than 1 ppm for some dyes), it is highly visible (3). On an environmental level, the presenceof coloring material in water system reduces the penetration of light, thereby affectingphotosynthesis in aquatic planktons (4). Some of these dyes are also toxic as well as carcinogenic andthis poses a serious hazard to aquatic living organisms. Sulfur dyes, for example, can rapidly reduceoxygen content of the water and are catastrophic for aqueous organisms. Most dyes increase theacidity of the water system and as a result not only kill fish and other aquatic life but also damageagricultural land and crops. In addition, the toxic compounds from dye effluent have been shown tobioaccumulate through aquatic food chain as well as cause several physiological and biochemicalchanges in fish (5). For humans, dyes have been linked to increased heart rate, nausea, vomiting,shock, cyanosis (blue baby syndrome), jaundice and quadriplegia (6).This problem is exacerbated bythe fact wastewater containing dyes are very difficult to treat, since the dyes are recalcitrant organicmolecules, resistant to aerobic digestion, and are stable to light, heat and oxidizing agents (3). 7
  8. 8. Ashu Tamrakar CHE 391: Independent StudyIn order to combat the increasing dye pollution, several environmental preservation efforts andfederal regulations have been put in place to restrict the industrial effluent. In 1972, title III of theFederal Water Pollution Control Act established the Effluent Guidelines Program (EGP) which wasamended first in 1977 by the Clean Water Act Amendments and subsequently in 1987 by the WaterQuality Act (7). The EGP, implemented by the Environmental Protection Agency (EPA) sets thelimits for the amount of discharge for toxic compounds including organic and inorganic dyemolecules, legally allowed to be returned to the environment. Another program called the NationalPollutant Discharge Elimination System (NPDES) strives to control water pollution by issuingpermits for industries that discharge pollutant into the waters of United States (7).With the need to comply with such stringent discharge standards and to achieve an optimum controland management of dye effluent, new concepts involving effective dye removal designs have beendeveloped around the world. During the past three decades, several physical, chemical and biologicaldecolorization methods have been reported in literature that attempt to showcase the besttechniques for dye removal. Amongst the numerous techniques, adsorption of dye onto differentcompounds has been shown to give the best results as it can be used to remove different types ofcoloring materials (2). This paper provides an overview of the dye adsorption process and studiesthe possibility of developing an efficient predictive model of dye removal that will help in optimizingthe adsorbent selection decisions.1.2 Research MotivationAs previously mentioned, adsorption is one of the most established unit operations used for thetreatment of contaminated wastewater and as such adsorption boasts of innumerable studies thattarget different kinds of dye removal. In general, these studies are usually conducted either inbatches or in adsorbent column experimentations. The batch studies are aimed at determining thekinetics and isotherm constants for the adsorption process while column studies are performed fordetermining the breakthrough curve that represent the dye concentration as the wastewater leavesthe adsorbent bed. In both types of studies, the dye removal efficiency is the most significant outputand the variation of this efficiency depends on several factors such as adsorbent characteristics, 8
  9. 9. Ashu Tamrakar CHE 391: Independent Studycontact time, initial adsorbate (dye) concentration etc. However, even with the large availability ofexperimental studies that provide information on the dye removal efficiency of adsorbents, orperhaps precisely due to its extensive nature, it is extremely difficult to rank the adsorbents andhence decide the best one to use with a particular effluent stream. This dilemma is compounded bythe fact that there are no specific guidelines available to assess the suitability of the adsorbent for thecontaminated water treatment. Most of the adsorption studies are not carried out within a standardrange of parameters and when investigators do develop new indigenous adsorbents, more often thannot the physio-chemical characteristics of the absorbents are not reported. Basu et. al. highlightsthese underlying issues with the observations and characteristics that are being reported in theseliterature studies in terms of (8): • Inconsistency in the characteristics of several adsorbents that are being reported • Insufficiency in the information to completely understand the adsorption mechanism when a database is generated for similar characteristics/ trends in adsorptionTo put it simply, the results that are reported by an investigator may be accurate and sufficient fortheir study (i.e., on a micro level) but when they are compared on a common basis (i.e., macro level)to other adsorption studies, with respect to the adsorbate /adsorbent/specific characteristic propertyetc., inconsistent trends in the results are observed (8). In fact, majority of the authors report that itis extremely difficult to compare any indigenous data with that already published in literature. Thisdifficulty along with the dire need to optimize the dye removal system led to this development of amodel to predict the dye removal efficiency from batch studies database of adsorption fromliterature. The core assumption for this investigation is that the database reported in the literature isaccurate at the micro level.1.3 Neural Network in adsorption kinetic studyDeveloping a predictive model that will compare the relative dye removal performance of severaladsorbents over a wide range of parameters is not a straight forward task. Although the efficiency ofeach adsorbent can be modeled using isotherms, it is difficult to understand the relationshipbetween the efficiencies of several adsorbents and the interconnectedness of variables that 9
  10. 10. Ashu Tamrakar CHE 391: Independent Studydetermine the removal performance. It becomes, thus, imperative to tackle such problems usingtechniques outside the conventional chemical engineering curriculum.Different techniques are being used in the computational community for prediction tasks such asthe dye removal model. In recent years the concept of artificial neural networks (ANN) which is atype of artificial intelligence technique has emerged as one of them. A neural network, in general, isable to work with just input dataset to find patterns and irregularities as well as to detect multi-dimensional non-linear connections in data. The latter quality is extremely useful for modelingdynamical systems, such as the dye adsorption process. ANNs have the ability to relate the input andoutput variables without having any knowledge on physics of the system provided an accurate andlarge amount of data on the system variables to train the networks is available. The neural networkscan thus yield solutions to complex phenomena where the relationships and rules are not known.The use of neural networks in adsorption studies is not a new concept. Over the last four yearsalone, several researches such as Improving the Efficiency of Wastewater Treatment Process by SoftComputational Methods (1), Modeling of Nitrate compounds on granular activated carbon (CAG) using artificialneural network (ANN) (9), Neural Network Modeling and Simulation of the Solid/Liquid Activated CarbonAdsorption Process (10) and Artificial Neural Network (ANN) approach for modeling of Pb(II) adsorption fromaqueous solution by Antep pistachio (Pistacia Veral L.) shells (11) have made use of neural networks tomodel the adsorption behavior. The application of neural network in these studies, however, havebeen more for extrapolation of experimental results of a single adsorbent-pollutant combinationrather than to develop a comprehensive predictive model of several adsorbents. Trained on the dataobtained from the limited number of physical experiments, the neural networks in these researchesallow for prediction of adsorption efficiencies under novel conditions.Fortunately, there are a handful of researchers who have attempted to build an adsorption modelwith more than one set of adsorbent-pollutant combinations. Basu, Ramakrishna and Chakravarthyfrom the Birla Institute of Technology and Science, India are at the forefront of this endeavor andhave applied ANN to the literature data pertaining to adsorption batch studies of 25 adsorbents 10
  11. 11. Ashu Tamrakar CHE 391: Independent Studytested over 8 different pollutants namely Chromium, Zinc, Copper, Mercury, Cadmium, Lead, CODand color (12). The group trained their ANN using 440 literature data points with 7 independentvariables (adsorbent material, pollutant type, equilibrium time, pH, contact time, adsorbateconcentration, and adsorbent dose), and tested the network with 73 data points. However, the focusof their study is more towards the development of a strong neural network architecture withoptimization of the ANN parameters such as number of hidden layers, learning rate, number ofepoch, etc. rather than finding the optimal adsorbent to use with a given pollutant problem. Thisproject, on the other hand, will attempt to not only train ANN with a database of wide range ofadsorbents as reported in literature (as many as 22 different adsorbents, only one dye) but also willapply the establish neural network to rank the adsorbents tested . The ranking developed will helpstreamline the decision making process for the selection of best adsorbent to use at a given dyecontamination condition.1.4 Research AimThe main objective of this project, therefore, is to formulate a functional relationship between theefficiency (output) and the adsorption factors (inputs) for the adsorption of Methylene blue dyefrom aqueous solutions. The factors considered in this study are material of adsorbent, pH, contacttime, initial concentration of dye in solution, temperature of solution and the adsorbent dosage.There are both dimensional variables (contact times, temperature, initial concentration of adsorbate,& adsorbent dosage) and dimensionless groups [material of adsorbent and pH] in these factors.Because of the complexity of non-linear relationship and incomplete understanding between thedimensional/ dimensionless variables and the efficiency as well relativity between the adsorbents,predictive modeling through ANNs is proposed. In addition, the neural network thus developed willbe used to rank the adsorbents in various conditions such as high/ low pH wastewater, high/lowtemperature conditions, etc and the best ones to be used in each condition will be identified.1.5 Paper OrganizationIn order to accomplish the above mentioned objectives, the project was carried out in two mainphases: Phase I – NN development (carried out in Fall 2011 semester) and Phase II- NN application 11
  12. 12. Ashu Tamrakar CHE 391: Independent Study(Spring 2012). The descriptions of these phases as well as the necessary theory related to the projectare detailed in this paper over 6 chapters:• Chapter 2 presents an overview of dye removal process using adsorbents and the principlesrelated to the adsorption mechanisms.• Chapter 3 discusses the architecture and parameters associated with artificial neural networktechnique. This chapter also provides a literature review on application of neural networks inadsorption processes.• Chapter 4 discusses the first half of Phase I of the project which is database collection for theneural network development. The methodology of experimental data extraction from literature, thetest for quality of data and standardization is further discussed.• Chapter 5 presents the steps in development of the artificial neural network. Simulation results arepresented assessing the performance of neural network based on parameters such as number ofhidden layers and amount of training data set. Validation methods using both a validation sample setas well as visualization methods are also presented and discussed.• Chapter 6 gives an overview of the application of the neural network model developed, includingeffects of variables such as pH, dye concentration, temperature, etc on the dye removal efficiency. 12
  13. 13. Ashu Tamrakar CHE 391: Independent StudyChapter 2. Dyes and Dye Removal2.1 Colorants and DyesColorants are compounds that are characterized by their ability to absorb and emit visible light from400 to 700 nm (13). Broadly, colorants are classified into either dyes or pigments based on themanner in which they give color to materials. Pigments are generally insoluble compounds andhence are used to color substrates like textile, paper, plastic, etc by attaching to the substratesthrough use of additional compounds such as polymers. Dyes, on the other hand, are applied to thesubstrate via a liquid medium in which they are either completely or partially soluble and attach bybinding with the material. Chemically, dyes are ionic, aromatic organic compounds with structuresincluding aryl rings with delocalized electron systems (14). The characteristic color of a dye moleculeis given by the chromophore group present in the structure where the energy difference between thetwo different molecular orbitals falls within the range of visible spectrum (15).Dyes are widely used in a variety of industries including textile, paper, plastic foodstuff, cosmetics,pharmaceutical and mineral processing industries to color their products. In fact, the scale andgrowth of the dyes industry has been completely linked to that of the industries using the product.For instance, the annual production of textiles around the world is about 30 million tons and theamount of dye needed for this production rate is about 700,000 tons (16). Other estimations carriedout in 1994 predict the world dye production be closer to 1 million tons per annum (17).2.2 Classification systems for dyesThere are several established ways of classifying dyes including differentiating dyes based on theirchemical structure (chemical classification) or according to the method of application (dyeingmethod) or by nature of the electronic excitation of the dye molecule. The most popularclassification however is the one advocated in the US International Trade Commission (USITC)which divides dyes into the following types (13): 13
  14. 14. Ashu Tamrakar CHE 391: Independent Study Table 1. Classes of Dyes and Their Chemical Types (13) Class Substrate Method of Application Chemical types Azo (including Nylon, wool, silk, premetallized), Usually from neutral to acidic Acid paper, inks and anthraquinone, bath. leather. tryphenylmethane, azine, xanthene, nitro and nitroso. cyanine, hemicyanine, Paper, diazahemicyanine, polyacrylonitrile, diphenylmethane, Basic Applied from acidic dye baths. modified nylon, triarylmethane, azo, azine, polyester and inks. xanthene, acridine, oxazine and anthraquinone. Reactive site on dye reacts with functional group on fiber Azo, anthraquinone, Cotton, wool, silkReactive to bind dye covalently under phthalocyanine, formazan, and nylon. influence of heat and pH oxazine and basic. (alkaline). Applied from neutral or Cotton, rayon, slightly alkaline baths Azo, phthalocyanine, Direct paper, leather and containing additional stilbene, and oxazine. nylon. electrolyte. Fine aqueous dispersions often applied by high Polyester, temperature/pressure or Azo, anthraquinone, styryl,Disperse polyamide, acetate, lower temperature carrier nitro and benzodifuranone. acrylic and plastics. methods; dye maybe padded on cloth and baked on or thermo fixed. Plastics, gasoline, Azo, triphenylmethane, varnishes, lacquers,Solvent Dissolution in the substrate anthraquinone, and stains, inks, fats, oils, phthalocyanine and waxes. Aromatic substrate vatted with sodium sulfide and Sulfur Cotton and rayon Indeterminate structures reoxidized to insoluble sulfur- containing products on fiber Water-insoluble dyes solubilized by reducing with Anthraquinone (including Cotton, rayon and Vat sodium hydrogensulfite, then polycyclic quinines) and wool exhausted on fiber and indigoids reoxidized 14
  15. 15. Ashu Tamrakar CHE 391: Independent Study2.3 Methylene Blue (MB)The dye being examined in this project is Methylene Blue (also known as Basic Blue 9) which fallsunder the Basic dye category and is a dark green crystalline solid (18). The molecular structure ofMB is illustrated in Figure 1. Solutions of MB in water or alcohol have a characteristic deep bluecolor from which the compound derives its name. The annual production of MB was estimated bythe National Institute of Health (NIH) to be around 12-120 thousand pounds in 1977 (19). Figure 1. Molecular Structure of Methylene Blue dye (20)The common applications of MB are listed below (19): A. For Therapeutic Uses: i. Treatment of methemoglobinemia ii. Antidote for cyanide poisoning iii. Treatment of manic-depressve psychosis iv. Formerly used as urinary antiseptic (currently more effective agents are available) v. Formerly used as an analgesic, antipyretic and antiparasitic B. Use as a dye/stain: i. Bacteriologic stain ii. Indicator dye iii. Surgical and medical marking iv. Coloring paper, cotton, wool and leather v. Temporary hair colorantThe National Fire Protection Agency (NFPA) rates the Methylene Blue (MB) dye at a health risk of2 and it was officially nominated for carcinogenicity by the National Cancer Institute (NCI) in 1989(6). Data from the National Occupational Exposure Survey (NOES) indicate that 69,563 workers, 15
  16. 16. Ashu Tamrakar CHE 391: Independent Studyincluding 42,026 female employees were potentially exposed to Methylene blue between 1981and1983. The toxicological effects of MB on humans are summarized below (6): • Acute exposure to MB has been found to cause increased heart rate, cyanosis, vomiting, shock, Heinzbody formation, jaundice, quadriplegia and tissue necrosis in humans. In addition, corneal and conjunctival injury has been reported following acute exposure to this compound. Intravenous administration of methylene blue has been found to cause bluish discoloration of the urine and stool especially for the newborn. • Chronic application of Methylene blue-containing eyedrops has been found to result in staining of the bulbar and palpebralconjunctiva, the lid margins and slight staining of the cornealepithilium2.4 Methods of removing dye from wastewaterMore than 100,000 different types of commercial dyes are manufactured every year and it isestimated that about 2% of total dye produced annually is released in the effluent stream (14). Thesituation is even worse with textile industries where about 10% of dyes used are discharged intowaste stream (14). In order to safely treat the sewage to comply with government regulations severalbiological, chemical and physical methods of dye removal have been developed. These includephysical-chemical flocculation, electroflotation, membrane filtration, electrokinetic coagulation,precipitation, ozonation , adsorption, etc. Table 2 shows the advantages and disadvantages of eachof these processes.The biological treatment method is the most economic option compared to physical and chemicalprocesses and involves decolorization through use of fungal/ microbial degradation and adsorptionby living or dead biomass (13). The application of biological methods, however, is limited because ofthe sensitivity of the microorganisms. Chemical methods of dye removal, on the other hand, consistsof coagulation, electroflotation and oxidation processes which are generally very expensive and alsolead to accumulation of concentrated sludge (13). The physical treatment of dye contaminatedwastewater use membrane filtration and adsorption techniques for decolorization. However, thephysical methods often suffer from limited membrane/adsorbent lifetime issues which make theprocesses expensive since periodic replacements are necessary (13). 16
  17. 17. Ashu Tamrakar CHE 391: Independent Study Table 2. Existing and emerging processes for dye removal (2) Treatment Advantages Disadvantages Coagulant Simple, economically High sludge production, handling and Floccutant feasible disposal problems Slow process, necessary to create an Economically attractive, optimal favorable environment,Conventional Biodegradation publicly acceptable maintenance and nutritiontreatment treatment requirementprocess The most effective Ineffective against disperse and vat Adsorption on adsorbent, great, dye, the regeneration is expensive and activated capacity, produce a result in loss of the adsorbent, non- carbon high-quality treated destructive process effluent Remove all dye types, Membrane High pressure, expensive, incapable of produce a high-quality separation treating large volumes treated effluentEstablishedrecovery No loss of sorbent on Economic constraints, not effective Ion-exchangeProcess regeneration, effective for disperse dye Rapid and efficient Oxidation High energy cost, chemical required process No sludge production, Advanced little or no consumption Economically unfeasible, formation of oxidation of chemicals, efficiency by-products, technical constraints process for recalcitrant dyes Economically attractive,Emerging Selective regeneration is not Requires chemical modification,removal Bioadsorbents necessary, high nondestructive processprocesses selectivity Low operating cost, good efficiency and Slow process, performance depends Biomass selectivity, no toxic on some external factors (pH, salts) effect on microorganisms 17
  18. 18. Ashu Tamrakar CHE 391: Independent Study2.5 Adsorption ProcessAs seen from Table 2, adsorption techniques are one of the most established physical methods ofdye removal. Adsorption of dye onto various types of adsorbents is gaining this favor due to theirefficiency in the removal pollutants too stable for conventional methods such as biological treatment(14). In addition, this removal technique produces a high quality product (complete removal of dyeis possible) with sludge free clean operation which has further increased its popularity (13). Ingeneral, adsorption processes work through either physical adsorption or ion-exchange. Physicaladsorption occurs when weak intermolecular bonds such as Van der Waals, hydrogen and dipole-dipole develop between the dye and adsorbent while ion-exchange which is a chemical adsorptionoccurs when stronger bonds such as covalent and ionic bonds are established between the dyemolecules and adsorbents (14). The decolorization processes in both mechanisms depends on manyphysio-chemical factors such as dye/adsorbent interaction, adsorbent surface area, particle size,temperature, pH and contact time.Most commercial systems currently use activated carbon as sorbent to remove dyes in wastewaterbecause of its excellent adsorption ability. Activated carbon adsorption has been reported by the USEnvironmental Protection Agency as one of the best available control technologies for dye effluenttreatment (2). The efficiency of activated carbon in adsorption is due to its structural characteristics,porous texture and chemical nature (3). The dye removal performance for all activated carbons isdependent on the type of activated carbon used and the characteristics of the wastewater. Thus, likemany other dye-removal treatments, it is well suited for one particular waste system and ineffectivein another.2.6 Commercial Adsorbents versus Nonconventional AdsorbentsAlthough activated carbon is a preferred sorbent, its widespread use is restricted due to high costwhich gets steeper yet with increases with its quality. In order to decrease the cost of treatment,attempts have been made around the world to find inexpensive alternative adsorbents. Recently,numerous approaches have been studied for the development of cheaper and effective adsorbentsusing non-conventional, low-cost materials including natural supplies, biosorbents, and waste 18
  19. 19. Ashu Tamrakar CHE 391: Independent Studymaterials from industry and agriculture. Table 3 below shows example of commercial adsorbents aswell as the non-conventional adsorbents reported in literature. These materials could be used asadsorbents for the removal of dyes from solution. Some of the reported sorbents include claymaterials (bentonite, kaolinite), zeolites, siliceous material (silica beads, alunite, perlite), agriculturalwastes (bagasse pith, maize cob, rice husk, coconut shell), industrial waste products (waste carbonslurries, metal hydroxide sludge), biosorbents (chitosan, peat, biomass) and others (starch,cyclodextrin, cotton). Table 3. Examples of adsorbents used in wastewater treatment (2) AdsorptionType Adsorbent supplier or material Dye capacity (mg/g) Filtasorb Corporation (USA) Reactive orange 107 714Commerical Merck Co. (Taiwan) Reactive red 2 712.3activated carbons Chemviron carbon (UK) Acid blue 40 133.3 Calgon Corporation (USA) Direct red 28 7.69 Pine wood Acid blue 264 1176 Bagasse Basic red 22 942Non Rice husk Basic Green 4 511conventional Treated sawdust Basic green 4 26.9carbon materials Fly ash Alizarin sulfonic 11.21 Neem sawdust Basic violet 3 3.78 Activated bentonite Acid blue 193 740.52.7 Common adsorption isothermsThe adsorption of dye is conventionally modeled in literature using various adsorption isotherms:the two common types of isotherms encountered are Langmuir and Freundlich isotherms.Regardless of the type, both of these isotherms describe the capacity of the adsorbent as a functionof equilibrium concentration of dye in solution at a constant temperature. Brief description of eachmodel is described below (21): 19
  20. 20. Ashu Tamrakar CHE 391: Independent StudyA. Langmuir Isotherm: This isotherm is the most widely used model to obtain the maximum adsorption capacity produced from complete monolayer coverage of adsorbent surface. The isotherm equation gives the fractional coverage (Θ) in the form: 𝜃= = 1+𝑏𝐶 𝑞𝑒 𝑒 𝑏𝐶 𝑄𝑚 𝑒 [1] where, Θ = fractional coverage b = adsorption equilibrium constant, l/mg Qm = quantity of adsorbate required to form a single monolayer of adsorbent, mg/g Qe = amount adsorbed on unit mass of adsorbent, mg/g and Ce = equilibrium concentration, mg/ lEquation 1 can be rearranged to get linear form as shown in Equation 2. If the adsorption behaviorfollows a straight line for a plot of (Ce/qe) vs. Ce, the adsorption obeys Langmiur isotherm. = + � 𝑄 � 𝐶𝑒 𝐶𝑒 1 1 𝑞𝑒 𝑏𝑄 𝑚 𝑚 [2] B. Freundlich Isotherm: The Freundlich isotherm is a semi-empirical equation which is widely used to represent adsorption equilibrium data for low to intermediate range of 𝑞 𝑒 = 𝐾 𝑓 𝐶 𝑒𝑛 concentration. The Freundlich equation is characterized below: [3] where, n = Freundlich coefficient and, Kf = Freundlich constant, mg1-1/n l 1/n g-1 20
  21. 21. Ashu Tamrakar CHE 391: Independent StudyChapter 3. Artificial neural networks3.1 Introduction to ANNThe neural network technique is an artificial intelligence technique that attempts to mimic thehuman brain’s problem solving capabilities. ANNs are analogous to the biology of a human brain,where billions of neurons are interconnected to process a variety of complex information (9). Whenpresented with data patterns, sets of historical input and output data that describe the problem to bemodeled, ANNs map the cause-and-effect relationships between the model input data and outputdata. This mapping of input and output relationships in the ANN model architecture allowsdeveloped models to be used to predict the value of the model output parameter, given anyreasonable combination of model input data, with satisfactory accuracy (4). With the advances incomputing power, ANNs have become extremely fast and flexible.Presently, artificial neural networks have been successfully applied in many fields, which includecharacter recognition, speech recognition, image processing, and stock performance prediction. Inchemical engineering, ANNs were found to be successfully applied to predict adsorption equilibriumof solid/liquid systems, activity coefficients of aromatic organic compounds, and solubility ofproteins. (10)3.2 Neural Network architectureIn general, a neural net, as shown in Figure 2, is parallel interconnected structure consisting of inputlayer of neuron (independent variables), a number of hidden layers, and an output layer (dependentvariables). The number of input and output neurons is fixed by the nature of the problem. However,the hidden layers, which act like feature detectors, can be adjusted as needed. 21
  22. 22. Ashu Tamrakar CHE 391: Independent Study Figure 2 Optimal ANN structure, together with a BP algorithm for the prediction of the Pollutant Removal Efficiency (PRE) (11)Besides the layers, an ANN model also consists of a pattern of connectivity or weights betweenunits (illustrated by the red lines in Figure 1) , a propagation rule for propagating patterns ofactivities through the weights, and a learning rule whereby weights are modified by experience (4).Depending on the ANN software being used, some or all of these components may be adjusted.Commonly employed neural network are generally feed-forward networks where the model inputdata is processed forward through the network in sequential fashion. The network prediction errorinformation may, however, be propagated in a backward direction through the network. 22
  23. 23. Ashu Tamrakar CHE 391: Independent Study3.3 Network LearningArtificial neural networks learn by reorganizing their internal structure according to a learning rule oralgorithm to minimize the error between the actual output value and the model-predicted outputvalue for the entire set of data patterns (4). Because the error is propagated backwards to adjust theweights on the input, the learning is commonly referred to as Back Propagation (BP) algorithm.Considering the model illustrated in Figure 2, the learning process for the particular network followsthe following sequence: a. Initially, the input parameter information (from the 5 input variables) is scaled in the input layer according to a scaling function. Typical scaling functions are linear and scale the values of all the input parameters to a common range, generally 0 to 1. b. Each input layer neuron is then connected to each of the 11 hidden layer neurons by a connection weight. Weights here are mathematical constructs that assign a numerical value to the importance of the connection between neurons. The output from each input neuron is thus multiplied by the appropriate connection weight and the resulting products are transferred to the hidden layer neurons. c. In the hidden layer, each neuron sums the value of the incoming products and processes the sum through a predefined activation function, which defines the neuron’s state of activity. d. Output values from each of the hidden layer neurons are then multiplied by the appropriate weights, as before, and the resulting products are transferred to the output layer. In the output layer, which has one neuron for each output parameter, each neuron sums the value of incoming products and maps the sum into an output value according to a predefined scaling function. e. The resulting model predicted value of the output is then compared with the actual value of the model output parameter from the data pattern. The output units then backpropagate the prediction error to the hidden layer according to a learning algorithm. f. Finally, the hidden layer units modify their incoming connection weights according to the learning algorithm to reduce the prediction error.The entire process is repeated over several iterations each of which are termed epochs until theANN produces a sufficiently small error, or other conditions as determined by the user are met. 23
  24. 24. Ashu Tamrakar CHE 391: Independent StudyThere are two main termination steps that can be controlled to stop the iterations: late and earlystopping. Late stopping refers to the termination condition where the network is trained until aminimum pre-specified error on the training set is reached. The minimum error on the training setdoes not always indicate the best results as more often than not the network is clearly over fittedwhich gives it a poor generalization ability. The concept of early stopping, on the other hand, refersto the condition where the learning progression is monitored over each epoch and the training isterminated as soon as signs of over fitting appear. The presence of over fitting is generally tested byusing a novel set of data points on the network after each epoch and measuring the error for itsprediction. As soon as the error stops decreasing for this new set of test data, the training isterminated. A clear advantage with early stopping is that the time of training is relatively short.3.4 Methods for Validation of Neural NetworksSince the internal computation of model function is not reported by a neural network, it becomesimperative to validate the network once it is trained. One of the simplest methods of validation thatare used by most developers is to separate a set of available data into three sets: training, validation,and testing sets (22). The training data set is then used as the primary set of data that is fed to theneural network during the training phase for learning and adaptation. The second set of data,confusingly called the validation dataset, is used to further refine the neural network learning bydetermining the termination step for the iterations (as described in the section above) rather thanactually validating the network. The main validation of the neural network is, thus, carried out byapplication of the developed neural network onto the testing dataset. Since the testing data pointsare novel to the network they can be used to determine the performance of the neural network byobserving the computation of an error between the networks’s predicted output and experimentalresult. . Figure 3 shows a representative performance plot of the neural network developed.In Figure 3, the blue line illustrates the decreasing error on training data while the green line showsthe error in the validation set. The training for the network stops when validation error stopsdecreasing or when it reaches the desired error tolerance. The red line indicates the error on the testdata which gives the information on how well the network will generalize to new data. 24
  25. 25. Ashu Tamrakar CHE 391: Independent Study Figure 3.Training, validation and test mean squared error for the Levenberg-Marquardt algorithm3.5 Advantages and disadvantages of ANN (4):Advantages: a. Since the data processing occurs purely from the data inputs to the system, no preexisting mathematical/ mechanistic models or assumptions are needed. b. The ANN technique is fault-tolerant in model development and thus accommodates discontinuities in the data, different levels of data precision, noise, and data scatter are easily accommodated. 25
  26. 26. Ashu Tamrakar CHE 391: Independent Study c. The technique is also extremely fast and flexible; advances in computing power have minimized the time required to develop models, as well as the time required to re-train models to incorporate new data and to reflect process modifications.Disadvantages: a. Since ANNs do not yield explicit mathematical formulae, many researchers consider the developed models to be “black-box” models. b. Little is known about the applicability of the models to data that lie outside the domain on which the models were trained. c. No set protocol for developing ANN models exists; each modeler may incorporate different modeling techniques. d. The ANN technique is data intensive and is best suited to problems where large data sets exist. 26
  27. 27. Ashu Tamrakar CHE 391: Independent StudyChapter 4. Database Collection4.1 VariablesIn order to model dye adsorption kinetics the following variables were examined: pH of the solution Initial concentration of dye (mg/L) Independent Variable (Input) Contact time (min) Adsorbent dose (g/L) Temperature of solution (oC ) Dependent Variable (Output) Dye Removal Efficiency (%)4.2 Adsorbents studiedIn the present study, thirteen different low cost adsorbents such as activated wheat bran, activatedclay minerals, sawdust, bio-adsorbents, etc used to remove MB from wastewater are investigated inthis report (#1-13, see Table 1). In addition, their adsorption capacity is compared with thecommercially available activated carbon. Additionally, a brief cost analysis is carried out for thedeveloped and commercial adsorbent which shows an economic feasibility of developed adsorbentsfor the removal of MB.Since the quality of the neural network is dependent on the quantity and quality of data it is trainedon, sufficient data mining for adsorption kinetics is a large part of the project. The main criteria usedto specify the boundaries of a source dataset is that it must be fully representative of the fullspectrum of possible conditions to which the model will be applied. In modeling dye removalprocesses to treat different volumes of effluent industrial water for example, the source data setshould be selected to encompass the levels of initial dye concentration encountered. This sectiondiscusses the methods of data extraction used and the subsequent quality of data. 27
  28. 28. Ashu Tamrakar CHE 391: Independent Study4.3 Data SourceArtificial neural network are initially developed and trained using historical data. The datasets usedfor training chemical process models, such as adsorption kinetics, are usually collected by physicalexperimentation by the researcher. Fortunately, there are abundant quantities of research carried outaround the world to study MB dye kinetics that is completely feasible to develop an ANN by usingonly information published in scientific journals. A complete list of articles used as source data forthis project is provided in Table 4. Table 4. List of adsorbents and source article for the development of ANN modelID # Adsorbent used Journal Article Title Authors Kinetic and Equilibrium Studies of Methylene O.S. Bello; O. M. Adelaide; Treated1 Blue Removal from aqueous Solutions by M.A. Hammed; O. A.M. Sawdust adsorption on treated sawdust Popoola Removal of Methylene Blue, a basic dye from S. Patil; S. Renukdas; N.2 Teak Tree bark aqueous solutions by adsorption using teak Tatel tree bark powder Fast Removal of Methylene blue from aqueous Chitosan CTS- L. Wang; J. Zhang; A.3 solution by adsorption onto chitosan-g-poly g-PAA Wang (acrylic acid)/ attapulgite composite Fast Removal of Methylene blue from aqueous Chitosan CTS- L. Wang; J. Zhang; A.4 solution by adsorption onto chitosan-g-poly g-PAA/APT Wang (acrylic acid)/ attapulgite composite Experimental study of Methylene blue Carbon Z. Shahryani; A. S.5 adsorption from aqueous solutions onto Nanotubes Goharrizi; M. Azadi carbon nanotubes Acid-activated Removal of Methylene Blue from aqueous B. Karima; B. L. Mossab;6 Algerian solutions using an acid activated Algerian M. A-Hassen Bentonite Bentonite: Equilibrium and kinetic Studies Removal of Methylene Blue from aqueous Oualid Hamdaoui, Mahdi7 Wheat Bran solutions by Wheat Bran Chiha8 Biosolids Removal of Methylene Blue by using biosolids M. Sarioglu, U.A. Atay The removal of cationic dyes using coconut9 Coconut husk K.S. Low and C.K. Lee husk as an adsorbent 28
  29. 29. Ashu Tamrakar CHE 391: Independent Study Removal of basic Dye Methylene Blue fro Walnut R. Ansari and Z.10 aqueous solutions using sawdust and sawdust Sawdust Mosayebzadeh coated polypyrrole Walnut Removal of basic Dye Methylene Blue fro Sawdust coated R. Ansari and Z.11 aqueous solutions using sawdust and sawdust with Mosayebzadeh coated polypyrrole Polypyrrole Water-washed A kinetic, thermodynamic and mechanistic K.M. Parida, Swagatika manganese approach toward adsorption of Methylene12 Sahu, K.H. Reddy and P.C. module leached blue over water-washed manganese nodule Sahoo residue leached residues Sunflower stalks as adsorbents for color Gang Sun and Xiangjing13 Sunflower stalk removal from textile wastewater Xu Titanium Sportive removal of dyes using titanium Kalpana C. Maheria and14 Phosphate phosphate Uma V. Chudasama Araceli Rodriguez, Gabriel Removal of Dyes from wastewaters by Ovejero, Maria Mestanza15 Sepoilote adsorption on Sepiolote and Pansil and Juan Garcia Araceli Rodriguez, Gabriel Removal of Dyes from wastewaters by16 Pansil Ovejero, Maria Mestanza adsorption on Sepiolote and Pansil and Juan Garcia Evaluation of Loofa as a sorbent in the N.A. Oladoja, C.O.17 Loofa (fruit) decolorization of basic dye contaminated Aboluwoye and A.O. aqueous system Akinkugbe Raw Sugarcane Kinetics study of methylene blue dye S. P. Raghuvanshi1, r.18 baggase bioadsorption on baggase Singh, c. P. Kaushik Chemically Treated Kinetics study of methylene blue dye S. P. Raghuvanshi1, r.19 Sugarcane bioadsorption on baggase Singh, c. P. Kaushik baggase P. Waranusantigul; P. Kinetics of basic dye (Methylene blue) Giant Duck Pokethitiyook; M.20 biosorption by giant duckweed (Spirodela weed Kruatrachue; E.S. polyrrhiza) Upatham Sulfuric acid Basic dye (Methylene blue) removal from treated Indian simulated wastewater by adsorption using V.K. Garg; M. Amita, R.21 rosewood Indian Rosewood sawdust: a timber industry Kumar; R. Gupta sawdust waste Formaldehyde Basic dye (Methylene blue) removal from treated Indian simulated wastewater by adsorption using V.K. Garg; M. Amita, R.22 Rosewood Indian Rosewood sawdust: a timber industry Kumar; R. Gupta Sawdust wasteNote: Adsorbents #3,4,5 and 14 are commercial type of adsorbents while the rest of the adsorbents were developed indigenously by the researcher. 29
  30. 30. Ashu Tamrakar CHE 391: Independent Study4.4 Data Extraction ProcessThe difficult part about collecting data from published articles is that the information on theadsorbent performances is usually presented graphically. In order to the extract the experimentaldata points from the graphs presented in the articles, GetData Digitizer evaluation version 2.24 wasused. This software essentially maps out the graph into a grid and collects the coordinates for thepoints/ line selected. The steps for extracting data points from a sample graph of temperatureeffects on the kinetics of dye removal by wheat bran, for example are explained below: Figure 4. Extraction process for getting data points from a graphStep 1: Set the scale for the grid by setting four points Xmin, Xmax, Ymin and Ymax, and by assigning logical coordinates to these points .In Figure 4, Xmin and Xmax are assigned values 0 and 200 respectively while Ymin and Ymax are assigned values 1.5 and 2.3. 30
  31. 31. Ashu Tamrakar CHE 391: Independent StudyStep 2: Since multiple experiments are presented in the graphs, in order to maintain the quality of data, the each trial should be manually digitized by clicking on the respective point. Figure 4 shows all the data points for the trail run at 50 C highlighted.Step 3: All the respective coordinates are displayed on the right column and can be exported to an Excel document.The data extraction process was carried out for all of the experiments, the range and quality of dataextracted is detailed in the next section.4.5 Ranges of input data collectedIn total 1363 data points from various combinations of inputs and their respective dye removal %were collected for the 16 adsorbents. Table 5 shows the statistical indexes of input and output datacollected. The original data collected for each adsorbent is available in Appendix A. Table 5. Statistical Index of Input and Output Data Inputs Outputs Initial Contact time Temperature Adsorbent % Dye Concentration pH (min) (C ) Dose (g/L ) Removal (mg/L) Min 5.0 1.1 0 20.0 0.0 0 Max 2201.5 12.3 2889.8 65 15.0 100 Mean 195.9 6.7 167.4 25.9 3.3 73 Median 100.0 7.0 60.0 25 4.0 81.3 S.D 364.3 1.6 371.1 6.4 2.6 25.3 95% CI (215.3,176.6) (6.8,6.6) (187.1,147.7) (26.3, 25.6) (3.4,3.1) (74.4,71.1)From Table 5, it is clear that most of the data collected from the adsorbents lie in a very small rangeof pH, initial concentration, contact time, temperature as well as adsorbent dose values. However, 31
  32. 32. Ashu Tamrakar CHE 391: Independent Study the spread of the data, which is more important as it establishes the connection between each variable, is not visible in the statistical indices. Figures 5-9 below shows the distribution of each variable for each adsorbent. 11.0 10.0 9.0 8.0 Figure 5. 7.0 pH rangeRange of pH in 6.0 the dataset 5.0 collected 4.0 3.0 2.0 1.0 0.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Adsorbent Type 500.0 Initial Dye concentration (mg/L) 450.0 Figure 6. 400.0Range of initial 350.0 dye 300.0 concentration 250.0 in the dataset 200.0 collected 150.0 100.0 50.0 0.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Adsorbent Type 32
  33. 33. Ashu Tamrakar CHE 391: Independent Study 500.00 450.00 400.00Contact Time (min) 350.00 300.00 Figure 7. 250.00 Range of 200.00 contact time in the dataset 150.00 collected 100.00 50.00 0.00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Adsorbent Type 50.0 45.0 Temperature Range (C) Figure 8. 40.0 Range of temperatures in 35.0 the dataset collected 30.0 25.0 20.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Adsorbent Type 16.00 14.00 Adsorbent Dosage (g/L) 12.00 Figure 9. 10.00 Range of 8.00 adsorbent dose in the dataset 6.00 collected 4.00 2.00 0.00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Adsorbent Type 33
  34. 34. Ashu Tamrakar CHE 391: Independent StudyAs can be seen from the Figures above, despite the poor statistical indices, the distribution of dataacross pH, dye concentration, contact time and adsorbent dosage ranges is actually reasonable.However, it must be noted that the adsorption experiments across temperature ranges were verydifficult to find and hence the results of the adsorbent performance across temperature conditionsmight not be as accurate as other variables.4.6 Data PreparationA database (see Appendix A) of 1363 data points comprising of the above variables is collected andcompiled. The final step of the database collection phase is data preparation for neural networkthrough normalization. The database is normalized to suit the input requirements of ANN using theformula: 𝑋 = (𝑋 𝑖 −𝑋 𝑖,𝑚𝑖𝑛 ) 𝑖,𝑛𝑜𝑟𝑚 (𝑋 𝑖,𝑚𝑎𝑥 −𝑋 𝑖,𝑚𝑖𝑛 ) [4] where, Xi= original value of the variable i Xnorm= normalized value of the variable Xmax = maximum value of the variable and, Xmin = minimum value of the variableThis normalized data is then used for training the network such that, the data will lie in the range of0 to 1.0. Arbitrary numbers are assigned to all 22 adsorbents to facilitate in normalizing the dataavailable for the type of adsorbent. 34
  35. 35. Ashu Tamrakar CHE 391: Independent StudyChapter 5. NN modeling of adsorption5.1 Model DevelopmentThe first step in the development of neural network is the formulation of the adsorption model touse with the dye removal behavior. As previously discussed, many adsorption isotherm systems suchas the Langmuir and Freundlich isotherms have been developed over the years to describe theadsorption capacity of sorbates. Although these classic adsorption isotherm models are capable ofrepresenting equilibrium adsorption data sets the variable nature of adsorption behavior across thetype of adsorbents being used in this project presents a challenge to the development of an equationthat can be used to model the behavior of all adsorption processes. Thus, a general adsorptionmodel must be considered to successfully predict the trends between the dye removal efficiency ofdifferent adsorbents.For batch studies of adsorption data extracted from literature, the MB dye removal efficiency, DRE(%) is described by the following function for this study: DRE % = f (adsorbent material, pH, T, tc, Cd, Co) [5] where, T = Temperature of the solution, C tc = contact time of adsorption, minutes Cd = adsorbent dosage, g/mL and, Co = initial concentration of the dye, mg/mLIn this study, Neural Network Toolbox v.4.0 of MATLAB® mathematical software was used topredict the adsorption efficiency using the normalized dataset prepared from the database collectionphase. 35
  36. 36. Ashu Tamrakar CHE 391: Independent Study5.2 Optimization of the NN architecture Figure 10. Neural network schematic for dye adsorption modelFor the present problem, there are six inputs and one output (DRE) variable. The back propagationnetwork is systematically trained and tested using various combinations of dataset distribution (seesection 3.4) and number of hidden layer to identify the optimum architecture for the network. Theoptimal architecture of the ANN model and its parameter variation were determined based on theminimum value of the R2 of the testing data set prediction set varying following parameters: • Number of neurons in the Hidden layer: • Distribution of data set into training set, validation set and test setFigure 11 shows the contour plot of the neural network performance in terms of R2 correlation ofnetwork predicted and actual experimental results of the testing data set. The larger the value of theR2 in the figure the better the neural network is adept at predicting the value of DRE. It is evidentthat if the number of neurons in the hidden layer is increased, the performance of the networkincreases. However, after a limit the network becomes complicated and the performance plateausout illustrated by the green contours after the hidden layer is increased beyond 10 layers. In terms ofthe dataset distribution, the 90% separation of dataset into training set hands down gave the bestresults.The best NN performances were observed at 8, 200 and 400 number of hidden layers and 90%training dataset distribution. 36
  37. 37. 37 Figure 11. Dependence of Neural network performance on the number of neuron at hidden layer and the distribution of dataset separated for training the neural network. CHE 391: Independent Study Ashu Tamrakar
  38. 38. Ashu Tamrakar CHE 391: Independent StudySince the dataset distribution is being controlled randomly in the Neural network toolbox, itbecomes essential to check the reproducibility of the results obtained for the best NN architecturedetermined. Consequently, 12 trials of neural networks were developed for each of the three hiddenlayer- dataset distribution combination. Figure 12 show the performance of each combination interms of the R2 value of the testing data correlation. Figure 12. Reproducibility check for the best NN architecture combinationsFrom figure, the best NN performance are obtained for 200 and 400 hidden layer terms, however,since the amount of computation time increases exponentially with the increase of hidden layers,200 was chosen to be the optimal number of terms in the hidden layer for dye adsorption model. 38
  39. 39. Ashu Tamrakar CHE 391: Independent Study5.3 Present Study ResultsUsing 200 hidden layers and by using 90% of the normalized dataset collected as training data, aneural network model for the adsorption of MB dye by the 22 adsorbents was developed. Figure 13shows the performance of the network thus developed. Figure 13. Quality of NN predictions for the training, validation and testing dataset. The overall performance of the neural network shows 89% accuracy between predicted values and literature data. 39
  40. 40. Ashu Tamrakar CHE 391: Independent StudyThere are some important trends visible in the performance graphs that must be noted whenmoving forward with the neural network model i. The most important regression value to observe in the performance graphs is the correlation coefficient (R2) value for the test data. Since these data points were not trained, they represent the best accuracy of the model to predict new conditions. The network currently shows 86.4% accuracy in predicting the dye removal efficiency. ii. As can be seen in the training dataset graph (top left graph), the network seems to have the hardest time fitting values close to the low and high point for the variables. This is evident by the large discrepancy in the predicted output at target values close to 0 and 1. Since the exact data points that have caused the error are unknown, it is important to hence understand that the network predictions at upper and lower bound for any variable is not as accurate as the predictions for the range in between. This trend is also visible in the testing dataset where the prediction for the central region is significantly better than at the bounds. This trend might have caused the lower R2 values for the training and the test data.In general, this neural network provides a reasonable estimate of the individual adsorbentperformance of Methylene blue dye removal. 40
  41. 41. Ashu Tamrakar CHE 391: Independent StudyChapter 6. NN Applications6.1 Comparative predictions of variable effect on removal efficiencyOne of the simplest ways in which the neural network developed can be utilized is by predicting theperformance of adsorbents at different variable ranges. With the ability to now forecast the relativeefficiencies of each adsorbent at the same parameter range, a new possibility of ranking theadsorbents is available. A ranking thus developed would be very helpful in determining the bestsorbent to use in a particular waste water condition hence optimizing the decision process foreffluent treatment systems.For this project, a simple ranking model was developed by analyzing the dye removal efficiency (%)of each adsorbent at the given conditions. The ranks are as follows: 1st preference adsorbents: 80% - 100% dye removal 2nd preference adsorbents: 60% - 80% dye removal 3rd preference adsorbents: 40% - 60% dye removal 4th preference adsorbents: 20% -40% dye removal 5th preference adsorbents: 0% - 10% dye removalThis ranking was used to categorize the 22 adsorbents tested on the basis of their performanceunder each variable condition. The performances were first predicted by applying the neuralnetwork model to each adsorbent type by changing the variable of interest over a desired rangewhile keeping the other conditions constant. For instance, to look at how all the adsorbents wouldfare in a waste water of varying acidity, the pH input conditions from 1-11 were tested while keepingthe initial dye concentration at 195.9 mg/l, contact time at 167 mins, temperature at 25.9 andadsorbent dose at 3.3 mg/l. The contour plots of the dye removal efficiency for changes in the 5input conditions are illustrated in Figures 14-19 along with the best choice of adsorbent to use athigh and low conditions of the particular variable. 41
  42. 42. 42 Figure 14. Prediction for the performance of adsorbents at different pH levels.. The circled adsorbents are commercial carbons while the rest are non-conventional adsorbents. CHE 391: Independent Study Ashu Tamrakar
  43. 43. 43 Figure 15. Prediction for the performance of adsorbents at different initial dye concentration levels. CHE 391: Independent Study Ashu Tamrakar
  44. 44. 44 CHE 391: Independent Study Ashu Tamrakar Figure 16. Prediction for the performance of adsorbents at different contact times.
  45. 45. 45 CHE 391: Independent Study Ashu Tamrakar Figure 17. Prediction for the performance of adsorbents at different temperature conditions.
  46. 46. 46 Figure 18. Prediction for the performance of adsorbents at different adsorbate dosage conditions. CHE 391: Independent Study Ashu Tamrakar
  47. 47. Ashu Tamrakar CHE 391: Independent Study6.2 ResultsThe spectrum below visulizes the capacity of each adsorbent at different variable conditionspresented in the above figures. The color spectrum is dependant on the ranking of the adsorbent atthe condition specified. The detailed ranking is provided in Appendix C. Table 6. Ranking spectrum of each adsorbent at various variable conditionsID # pH Dye conc. (mg/l) Contact time (min) Temperature(C) Dose (g/l) 1-3 4-7 8-11 0 -20 30 - 2000 - 0 -20 30 - 2000 - 20-26 29-38 41-50 0-2 3-6 7-10 150 500 150 50012345678910111213141516171819202122Legend: 1st preference 4th preference 2nd preference 5th preference 3rd preference 47

×