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Two degree of freedom PID based inferential control of continuous bioreactor for ethanol production

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This article presents the development of inferential control scheme based on Adaptive linear neural network (ADALINE) soft sensor for the control of fermentation process. The ethanol concentration of bioreactor is estimated from temperature profile of the process using soft sensor. The prediction accuracy of ADALINE is enhanced by retraining it with immediate past measurements. The ADALINE and retrained ADALINE are used along with PID and 2-DOF-PID leading to APID, A2PID, RAPID and RA2PID inferential controllers. Further the parameters of 2-DOF-PID are optimized using Non-dominated sorted genetic algorithm-II and used with retrained ADALINE soft sensor which leads to RAN2PID inferential controller. Simulation results demonstrate that performance of proposed RAN2PID controller is better in comparison to other designed controllers in terms of qualitative and quantitative performance indices.

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Two degree of freedom PID based inferential control of continuous bioreactor for ethanol production

  1. 1. Research article Two degree of freedom PID based inferential control of continuous bioreactor for ethanol production Nikhil Pachauri n , Vijander Singh, Asha Rani Instrumentation and Control Engineering Division, Netaji Subhas Institute of Technology, University of Delhi, Sec-3 Dwarka, New Delhi 110078, India a r t i c l e i n f o Article history: Received 20 August 2016 Received in revised form 13 December 2016 Accepted 21 March 2017 Available online 27 March 2017 Keywords: Bioreactor ADALINE LMPNN BRBNN PID 2-DOF-PID a b s t r a c t This article presents the development of inferential control scheme based on Adaptive linear neural network (ADALINE) soft sensor for the control of fermentation process. The ethanol concentration of bioreactor is estimated from temperature profile of the process using soft sensor. The prediction accuracy of ADALINE is enhanced by retraining it with immediate past measurements. The ADALINE and retrained ADALINE are used along with PID and 2-DOF-PID leading to APID, A2PID, RAPID and RA2PID inferential controllers. Further the parameters of 2-DOF-PID are optimized using Non-dominated sorted genetic algorithm-II and used with retrained ADALINE soft sensor which leads to RAN2PID inferential controller. Simulation results demonstrate that performance of proposed RAN2PID controller is better in compar- ison to other designed controllers in terms of qualitative and quantitative performance indices. & 2017 ISA. Published by Elsevier Ltd. All rights reserved. 1. Introduction In a bioprocess large variety of products are manufactured with the help of living organisms. It is one of the complex processes in the field of process engineering because of its high dimensionality, nonlinearity and dynamic characteristics. Bioprocesses have gained an amazing interest of researchers in the last two decades as it plays an important role for the production of vaccines and antibiotics in pharmaceutical industries; beer, wine etc. in agro- food industries and in the treatment of industrial wastewater. Industrial fermentation processes have three modes of operations; batch, fed-batch and continuous. In a continuous operation sub- strate is added and product is removed continuously whereas in batch operations substrate is added at initial stage of process and final product is removed at the completion of the process. In case of fed-batch operation feed rate profile is varied during the pro- cess and final product is removed at the end of the process. The automatic optimal control of a bioprocess is of considerable in- terest to many fermentation industries, so as to decrease the production cost and maintain the quality of final product at the same time. However design of a control system for fermentation process is not an easy task due to model uncertainties, nonlinear nature of the system and slow response of the process. Further the lack of suitable and robust hardware sensor for measurement of biomass or product concentration is also a restriction for im- plementation of efficient control of fermentation process. The problem associated with the hardware sensors is the time delay for measurement due to which online control is not feasible. These sensors involve high cost, repeated calibration and regular main- tenance. Therefore hardware sensors are not an effective solution for precise online product quality measurement. Soft sensor is an alternative to hardware sensor, through which some process variables are measured online with an assessment algorithm in order to evaluate the unmeasured variables, model parameters and measurement delays [1]. Various researchers have extensively used soft sensors for concentration estimation in biochemical processes. Ödman et al. [2] used a partial least square (PLS) based soft sensor for the real time prediction of important analyte concentration such as bio- mass, glucose and ethanol. Experimental results show the effec- tiveness of PLS for real time prediction. Han and Qiao [3] pro- posed a self-organizing radial basis function (SORBF) neural net- work model for prediction of sludge volume index. Results confirm the robustness and effectiveness of SORBF as soft sensor. Warth et al. [4] proposed a soft sensor integration with NIR probe and high performance liquid chromatography (HPLC). The results ob- tained show that integration of NIR and HPLC increases the overall potential of soft-sensor for prediction of biomass concentration and other process parameters. Chen et al. [5] examined the ap- propriateness of Recurrent Neural Network (RNN) based online sensor for estimation of biomass concentration. Feed rate, liquid volume and dissolved oxygen are the input variables for sensor. It Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/isatrans ISA Transactions http://dx.doi.org/10.1016/j.isatra.2017.03.014 0019-0578/& 2017 ISA. Published by Elsevier Ltd. All rights reserved. n Correspondence to: Instrumentation and Control Engineering Division, Room No-119/VI, Azad Hind Fauz Marg, NSIT, Dwarka Sec-3, New Delhi 110078, India. E-mail addresses: nikhilpchr@gmail.com (N. Pachauri), vijaydee@gmail.com (V. Singh), ashansit@gmail.com (A. Rani). ISA Transactions 68 (2017) 235–250
  2. 2. is confirmed from the results that RNN is a powerful tool for im- plementing an online biomass soft sensor for fermentation pro- cess. Gustavsson and Mandenius [6] employed a soft sensor ap- proach for controlling the metabolic overflow for mixed acid fer- mentation and glucose overflow metabolism in fed-batch culti- vation. Two control strategies have been defined; first is control- ling the specific rate of both overflows with fixed preset target values, secondly concentration at a set level is controlled. The second control strategy is found to be more efficient in comparison to the first. Liu et al. [7] proposed a novel approach for optimi- zation of support vector machine model based on Genetic simu- lated annealing algorithm (GSA) and Akaike Information Criterion (AIC). Simulations show good performance of the proposed method for fermentation process. Tian et al. [8] used a neural network model based approach for the development of optimal control policies in which neural network is used for building a prediction model of fed batch bioreactor. Results show that pro- posed methodology overcomes the problems associated with first principle model. Veloso et al. [9] used an asymptotic observer as a soft sensor for monitoring of Escherichia coli fed batch fermen- tation. Experimental results verify the improved performance of proposed observer in comparison to extended Kalman filter. A surface plasma resonance (SPR) biosensor is employed (Vostiar et al.) [10] to monitor the profile of heat shock protein and hetero- logous protein to map the dynamics of cellular stress response in Escherichia coli. Results demonstrate the feasibility of using SPR in two channel protein array for intra cellular component monitor- ing. Two approaches are proposed by Ward et al. [11] for pre- diction of total alkalinity of anaerobic digester. These soft sensors are based on multiple regression algorithm and Near Infra-red spectroscopy (NIRS). Results reveal that NIRS method produces best model during validation of new data. Sharma and Tambe [12] developed a soft sensor based on genetic programming (GP) ex- tracellular production of lipase enzyme and bacterial production of 3-hydroxybutyrate-co-3 hydroxyvalerate. Performance of GP based soft sensor is then compared with MLP and SVR soft sensors. Results show the superiority of proposed soft sensor. Sagmeister et al. [13] proposed a novel soft sensor for investigation of mixed feed bioprocesses for control of the specific uptake rates of pri- mary and secondary substrate via amalgamation of inline spec- troscopy and rate base soft sensor. The performance of proposed soft sensor is then evaluated on a recombinant Escherichia coli pBAD mixed feed process. Results demonstrate that uptake rate increases more than 1 g/gh and adaptation time to L-arabinose is also reduced to 10 min. Wechselberger et al. [14] used soft sensor based on first principle for the real time estimation of biomass and specific growth rate for recombinant fed batch process. The pro- posed approach is superior in terms of gross error and sensor failure w.r.t other state of art methods. Results show the accuracy of developed sensor for biomass and specific growth rate estima- tion in real time. Vinod et al. [15] simulated a model of biode- gradation process in a fluidized bed bioreactor (FBR) using genetic algorithm trained feedforward neural network (FFNN). Pseudo- monas sp. micro-organism is used in the study. Results reveal that the diffusivities of phenol and oxygen in biofilm estimated from simulation are comparable with literature values. Other papers describe soft sensor based control of bioprocess for production of antibiotics in pharmaceutical industry (Bravo et al.) [16], online estimation of culture conditions and monitoring of recombinant protein in fully automated multistage production process (Frickle et al.) [17]. It is revealed from the literature that inferential or soft sensing techniques have gained tremendous attraction as a sustainable al- ternative to hardware sensors. Therefore the present research work focuses on the development of soft sensor for inferential control of ethanol concentration in a bioreactor. Diverse combinations of soft sensor based on multilayer perceptron neural network (MLPNN), genetic programming based neural network (GP-NN), partial least square method (PLS) etc., are developed for bioprocesses. These soft sensors are used to estimate the important variables based on secondary measurements, but these are not utilized for the control purpose. Further it is also revealed from literature that final product concentration of bioreactor is controlled indirectly by controlling the reactor temperature [18–20] using different control schemes. The problem associated with such type of control is that ethanol concentration depends not only on temperature of the reactor but also on other factors such as pH, dissolved oxygen content, sub- strate feed flow, specific growth rate of biomass etc. Therefore without the measurement of ethanol concentration the efficient control cannot be achieved. Due to the advancement in the field of soft sensor, it is possible to control and simultaneously monitor such parameters which are not readily available for measurement. Asha et al. [21] designed inferential control scheme based on ADALINE soft sensor and TL tuned PID controller for composition control of distillation column. The performance of ADALINE soft sensor is further improved by designing a dynamic ADALINE using past measurements. However in the present work conventional PID is replaced by non-dominated sorted genetic algorithm-II (NSGA-II) tuned 2-DOF-PID along with retrained ADALINE which leads to RAN2PID control scheme. The previously suggested in- ferential control scheme [21] is also applied for monitoring and control of ethanol concentration in bioreactor. It is observed that the performance of the proposed control scheme is better than the one available in literature. A rigorous literature survey also reveals that it is the first time RAN2PID control scheme is used for mon- itoring and control of fermentation process. The paper is organized as follows: A detailed mathematical modelling of bioreactor is discussed in Section 2. In Section 3 development of soft sensor using four types of neural network is discussed. Section 4 describes the inferential control scheme using developed soft sensor for control of ethanol concentration, Sec- tions 5 and 6 includes the discussion and conclusion of the re- search work respectively. 2. Mathematical model of continuous fermentation bioreactor Any real process can be represented in the form of a mathe- matical model. It encourages the control engineers to evaluate the performance of any new algorithm without affecting the real process. However to design mathematical model few assumptions are considered in order to make mathematical model realizable and computationally less complex. The control algorithms tested on the model may be implemented directly on the real process. It serves as an alternative to the real process and facilitates [22] the control engineers for the evaluation of controller tuning, de- termining the effects of disturbances, optimizing plant operation and investigating potential safety problems without disturbing the actual process. Thus dynamic mathematical models are essential, efficient and powerful tools for testing new control schemes. After getting the desired results on simulated model, the next step is to implement the scheme on the real time process. In a continuous fermentation bioreactor feed is continuously added to the system. The speed agitator for gentle stirring is chosen so as to keep cells in suspension, to provide sufficient mixing and to avoid extreme shear forces that may cause muti- lation of the cells. A product stream is removed continuously from the bioreactor. Fermentation of alcohol is one of the most popular and important bioprocess because of its product, ethanol. Ethanol is used as partial substitute for gasoline and is thus an alternate energy source. The bioreactor under consideration is a CSTR with following assumptions [18–20]. N. Pachauri et al. / ISA Transactions 68 (2017) 235–250236
  3. 3. Perfect mixing Constant stirring speed pH of the bioreactor is constant Constant substrate feed flow, the outlet flow from the reactor containing the product Input concentration of substrate and biomass are constant Bioreactor has three main constituents; biomass a suspen- sion of micro-organism (Saccharomyces cerevisiae, yeast); substrate, a solution of glucose on which microorganisms feed and the product (ethanol) is taken out continuously along with other reactor components. The dilution rate (Fe/V) for bior- eactor should be lower in comparison to production rate of biomass. The cell kinetics of the present model is based on modified Monod equations according to Michaelis-Menten kinetics presented by Aiba et al . [23]. μ μ= + ( ) −S K S e 1o s K P1 Fermentation processes have slow dynamics and inorganic salts are added to the yeast. These salts are responsible for the formation of co-enzymes and equilibrium concentration of oxygen is greatly affected along with reactor temperature. The mass balance equations for biomass, product, substrate and dissolved oxygen are as follows: μ= + − ( ) −dC dt C C K C e F V C 2 X X X s s s K C e X P P Where the maximum specific growth rate μX depends on the temperature and heat denaturation ( )μ = − ( ) −( ( + )) − ( + ) A e A e 3X E R T E R T 1 / 273 2 / 273a r a r1 2 μ= + − ( ) −dC dt C C K C e F V C 4 P P X s s s K C e P 1 P P1 In the above equations, first terms signify the quantity of bio- mass and product during fermentation, whereas the second terms represent the quantity of yeast and ethanol leaving the reactor respectively. ( ) μ μ= − + − + + −− − 5 dC dt R C C K C e R C C K C e F V C F V C 1 1s SX X X s s s KPCP SP P X s s s KP CP i s in e s 1 1 , Where first two terms represent the quantity of substrate gobble up by biomass for the progress and ethanol production respec- tively. Third term denotes the amount of glucose inflow to the reactor with new substrate feed, and last term is the amount of glucose exit from the reactor. The energy balance equation for reactor and jacket temperature are given as follows ( )( ) ( ) ( ) Δ ρ ρ = + − + + + − 6 dT dt F V T F V T r H C K A T T V C 273 273 32 r i in e r o r r heat r T T r ag r heat r, , 2 ( ) ( ) ρ = − + − ( ) dT dt F V T T K A T T V C 7 ag ag j in ag ag T T r ag j ag heat ag , , The concentration of dissolved oxygen depends on stirring speed and reactor temperature. As the stirring speed is as- sumed to be constant, so the variation in dissolved oxygen concentration is only due to the reactor temperature as shown in Eqs. (8)–(12). In the reaction medium concentration of dissolved oxygen is given by ( )= ( ) * − − ( ) dC dt k a C C r 8 O l O O O 2 2 2 2 The equilibrium concentration of oxygen in the liquid phase is ( )* = − + − * ( ) ∑− C T T T14.6 0.3943 0.00714 0.0000646 10 9O r r r H I2 3 i i 2 The global effect of ionic strengths are given as follows ⎛ ⎝ ⎜⎜ ⎞ ⎠ ⎟⎟ ∑ = + + + + + + + ( ) − −( − ) HI H m M M V H m M M V H m M M V H m M m M M V H m M M V H H 0.5 2 2 0.5 2 2 0.5 10 0.5 10 10 i i Na NaCl NaCl Na Ca CaCO CaCO Ca Mg MgCl MgCl Mg Cl NaCl NaCl MgCl MgCl Cl CO CaCO CaCO CO H pH OH pH14 3 3 2 2 2 2 3 3 3 3 The mass transfer coefficient for oxygen and rate of oxygen consumption are given by Eqs. (11) and (12) respectively ( ) = ( ) ( ) ( ) − k a k a 1.024 11l l T 0 20r μ= * * * + ( ) r Y C C K C 1 12 O O O X O O O 2 2 2 2 2 2 where Cs ¼ Glucose concentration CP¼ Ethanol concentration Fag¼ Flow rate of cooling agent Tin¼ Temperature of input flow Tr¼ Temperature of bio-reactor Te¼ Temperature of outflow Fi ¼ Input flow Fe ¼ Exceeding flow Tag¼ Temperature of cooling agent CO2 ¼ Dissolved concentration of oxygen The coolant flow rate (Fag) changes the reactor temperature (Tr), thereby changes the biomass concentration (CX) (Eq. 4). The ethanol concentration CP changes due to variation in CX (Eq. 4), hence it is observed that CP is indirectly affected by temperature profile of bioreactor. In a reaction medium the concentration of dissolved oxygen is also important for the growth of biomass, which directly affects the product concentration (CP). The funda- mental model parameters and nominal operating values are given in Appendix A and B respectively. The mathematical model dis- cussed above is simulated on Intels CoreTM i5 CPU with 2.4 GHZ frequency, 4 GB RAM in MATLAB version 8.0.1.604. Coolant flow rate of jacket is varied randomly and corresponding temperature and concentration variations are obtained. The simulation data thus obtained is used by soft sensors to model the input–output behavior of the system. 3. Development of soft sensor Soft sensor or virtual sensor is an ordinary name for software based sensors where various measurements are performed to- gether like fault detection along with estimation of unmeasured variables. The interaction of signals can be utilized for estima- tion of new quantities that cannot be computed. A soft sensor is a conceptual device whose inferred variable can be represented in terms of other parameters that are applicable to equivalent process. Neural network, Kalman filter, neuro-fuzzy are few examples of soft sensors. However an intelligent sensor involves N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 237
  4. 4. various intelligent functions for example self-testing, self- adaptation, self- identification etc. or it constitutes a sensing element and signal processor on a single chip [24]. In the pre- vious works Levenberg–Marquardt perceptron neural network (LMPNN) and Back Propagation neural network (BPNN) are used to develop soft sensor for chemical process. In [12] LM based soft sensor is used for comparing the prediction performance of proposed genetic programming based soft senor and Singh et al. [25] developed an LM based estimator for estimation of methanol concentration in binary distillation column. BPNN neural network is also suggested by Assis and Filho [26] for online state estimation in bioreactor. BPNN uses gradient descent method for training purpose. It suffers from slow convergence due to selection of step size, which should be suitable to gradient and secondly due to unsymmetrical error surface. The problem with BPNN is eliminated by LMPNN and BRBNN. The LMPNN executes a combinatorial training process which includes steepest descent and Gauss-Newton algorithm around complex curvature. Firstly LM algorithm shifts to steepest descent method and then to Gauss newton algorithm to speed up convergence. Further multilayer Bayesian Regularization back propagation based neural network (BRBNN) is used in the field of stock market forecasting [28] also designed for comparing the perfor- mance of proposed soft sensor. In BRBNN [27,29], Bayes rules are implemented to train the neural network in order to optimize regularization. In this neural network Gauss Newton method is applied to Hessian matrix and executed within the frame work of LM algorithm to reduce the computational complexity. In this paper a single layered neural network is used to build a soft sensor in order to predict the ethanol concentration by using easily measured process variables. In the procedure of building inferential measurement system an introductory set of input variables are selected on the basis of prior knowledge about the process. Two input variables are selected to infer the ethanol concentration. ( )= ( )C f T T, 13P r ag where CP ¼ Ethanol concentration Tag ¼ Jacket temperature Tr ¼ Reactor temperature Adaptive linear neural network or single layered neural net- work introduced by Bernard Widrow and Marcian Hoff in 1960, uses Least Mean Square learning algorithm instead of perceptron learning rule [30]. The perceptron rule provides guaranteed con- vergence to a solution that accurately classifies the training data, the trained network may become sensitive to noise. However the LMS algorithm minimizes mean square error, which shifts the decision boundaries far from the training patterns. Output of ADALINE network can be written as = ( * + ) = * + ( )x purelin p W b p W b 14 where Purelin is the linear transfer function which gives linear relationship between input and output of neural network. W is the weight matrix, b is the bias and p is the input data. In the present work ADALINE based soft sensing technique is compared with LMPNN, BRBNN and BPNN for prediction of ethanol concentration from temperature measurements 3.1. (a) Training and Testing of soft sensors The bioreactor is simulated by varying the flow rate of cooling agent to generate 3500 samples for training and testing of neural network. The data consists of reactor temperature (Tr), jacket temperature (Tag) of the bioreactor and the corresponding ethanol concentration (CP). ADALINE, LMPNN, BRBNN and BPNN are trained and tested with generated samples, and a predictive neural model is constructed. The desired prediction capability of soft sensors is achieved by minimization of the error between the predicted and the target value. Several suitable performance measures suggested in literature are used like root mean square error (RMSE), Mean absolute error (MAE), Relative square error (RSE), MSE, MPAE, MARE etc. However the comparison of three soft sensor models is carried out using MSE, MAPE and MARE. The usefulness and brief description of these error measures is given below: Mean square error (MSE) averages the squares of various errors. Due to squaring more weightage is given to large errors than smaller ones. Therefore MSE is used where large errors are pre- sented whose negative consequences are proportionately bigger as compared to smaller ones. = ∑ ( − ) ( ) MSE Y P Y 15 2 Mean absolute percentage error (MAPE) is defined as a per- centage of actual data and it is a relative measure. = ∑ − × ( ) MAPE Y P Y 100% 16 Mean absolute relative error (MARE) gives an indication of the average deviation of the predicted values relative to actual values. = ∑ ( − ) ( ) MARE Y P Y Y / 17 ( − )Y P Y/ Gives the value of absolute error relative to actual values; where Y and P are actual and predictive values respectively. Thus MSE provides the predictive accuracy of soft sensor w.r.t large errors whereas the biggest advantage of MAPE is its simpli- city and non-rational way of judging the extent of error. MARE is not contaminated by outliers present in the samples, lower the values of MARE, higher will be the predictive accuracy of soft sensor. All the performance measures are evaluated to measure the accuracy of the designed soft sensors in all aspects. The performance of any neural network heavily depends on the selection of hidden layers and number of neurons in each hidden layers etc., literature does not suggest any thumb rule for deciding the number of hidden layers and neurons in different layers. However several authors have proposed certain methods to eval- uate the number of hidden nodes but all are system specific [31,32]. The structure of the soft sensor models in the present work is optimized by following a systematic procedure. Initially trial and error is used to evaluate the neural structure for LMPNN, BPNN, and BRBNN. Two hidden layers are chosen first and number of neurons in each hidden layer is varied and network with minimum MSE is chosen for LMPNN, BPNN and BRBNN. Table 1 shows the MSE variation for different neurons in hidden layers for LMPNN, BPNN and BRBNN. The LMPNN architecture (2–20-20-1) with 2 neurons in input, 20 neurons in each hidden layer and 1 neuron in output layer is chosen as the number of neuron increases additionally MSE does not change much, further for BPNN (2–28-28-1) and BRBNN (2–24- 24-1) architecture are chosen. Whereas ADALINE is a single layered neural network for which the number of neurons depend upon the number of outputs. N. Pachauri et al. / ISA Transactions 68 (2017) 235–250238
  5. 5. The next step in the design of soft sensors is to optimize the size of training and testing set. It is revealed from the literature (Table 2) that no empirical rule is suggested for deciding the training and testing data set. Therefore different combinations of training and testing samples are tried and tested for the present work. Various error functions used for quantitative analysis of ADA- LINE, LMPNN, BPNN and BRBNN are given in Table 3 for different testing samples. As the training samples increase, the prediction accuracy of soft sensors also increases. The main aim of the soft sensor is to provide the better prediction with minimum error. Therefore 3450 samples are used for training and 50 samples for testing i.e. 98-2%. The predicted concentration by different soft sensors and the desired concentration are shown in Fig. 1. It is clearly observed from results that ADALINE estimated concentra- tion is almost coinciding with the desired concentration. It is also observed that the error between the target and estimated value are higher in case of and LMPNN, BPNN, and BRBNN as compared to ADALINE for testing samples. The good performance of ADALINE is further verified from the lower values of error functions in comparison to other neural networks. This is due to the reason ADALINE is a single layer neural network and uses a powerful learning algorithm, LMS. In this work two variables i.e. reactor temperature and jacket temperature are chosen as input to the soft sensor in order to infer the ethanol concentration. The MSE obtained after testing the soft sensor for these two inputs is 0.2596. The other interlinked parameters i.e. concentration of Dissolved oxygen (DO) and time are also considered as input variables along with the two temperature inputs to the soft sensor. The MSE with reactor temperature, jacket temperature and DO as inputs is 0.2592 and MSE after addition of time to the input is 0.2583. It is observed that MSE obtained does not show significant change. This is due to the reason that range of DO is very less and change in product concentration is almost linear with respect to DO. Therefore in the present work only two temperature inputs are considered so as to reduce the memory requirement and ex- ecution time. 3.2. (b) Validation of soft sensors Apart from training and testing, validation of soft sensors is also carried out. The previously designed soft sensors are validated for new dataset. Therefore 200 samples are chosen for validation purpose, which are different from training and testing data set used in the previous section. It is clearly observed from Fig. 2 that prediction of ADALINE for new samples is better and thus error is less in comparison to LMPNN, BPNN, and BRBNN. Table 4 shows the quantitative analysis of soft sensor for validation data set. It is revealed from the table that prediction accuracy of ADALINE is higher in comparison to LMPNN, BPNN and BRBNN. 3.3. (c) Robustness testing of soft sensors for real time data Soft sensors are extensively used in biochemical industries for prediction of unmeasured variables. In Section 3.1(a) and (b) the performance of soft sensors is tested and validated on the data obtained from simulation model. The bioreactor model under consideration is semi rigorous due to the as- sumptions considered for simulation. Further in order to relate the presented soft sensor to real time environment, the effec- tiveness of soft sensors is tested on real time data of industrial scale fed-batch fermentation process [37]. The mixture of lac- tose and yeast with some minerals and soybean oil are sup- plied to the fermenter. The agitator rotates and mixes the whole mixture properly. Soybean oil is utilized as antifoaming agent and additional carbon supplier. The flow rate of sugar and soybean is controlled using successive batch control, which uses a predetermined optimal profile. The vessel tem- perature and pH of the mixture should be maintained at 298 K and 6.5 respectively. The coolant and acid/base flow are the manipulating variables to control the temperature and pH. The fermentation process comprises 100,000 l bioreactor with 2.1 m radius and 100 rpm stirring speed. Various hardware sensors for continuous measurement of temperature, pH and dissolved oxygen concentration are mounted. Cooling coils are installed internally through which coolant flows [38]. The data utilized for evaluating the robustness of soft sensors consists of Table 1 MSE variation for LMPNN and BRBNN. No. of neurons in each hidden layer LMPNN (MSE) BPNN (MSE) BRBNN (MSE) 5 1.1120 1.5320 1.6032 8 0.9219 1.4780 0.8420 10 0.8591 1.3923 0.8431 15 0.8429 1.2143 0.9840 20 0.8324 1.1942 0.7892 22 0.8335 1.1732 0.5928 24 0.8334 1.1238 0.5616 26 0.8323 1.0982 0.5618 28 ……… 1.0523 0.5620 30 ……… 1.0643 ……… 32 ……… 1.0624 ……… Table 2 Size of training and testing data set in different literatures. Author's Training sample Testing sample Training and testing percentage Sharma and Tambe 2014 [12] 119 51 70–30% Asha et al. 2013 [21] 370 30 92–8% Raghavan et al. 2011 [33] 3000 Not specified Not specified Kenako and Funatsu 2013 [34] 100 1000 9–91% Wu et al. 2015 [35] 480 80 85–15% Bahar and Ozgen 2010 [36] Not specified Not specified Not specified Table 3 Quantitative analysis of soft sensors for different training and testing data set. Training and testing data ADALINE LMPNN (2–20-20-1) [12,25] BPNN (2–28-28-1) [26] BRBNN (2–24-24-1) [28] MSE MAPE MARE MSE MAPE MARE MSE MAPE MARE MSE MAPE MARE 70-30% 0.7858 6.4720 0.0639 0.9654 8.6432 0.0924 1.2342 8.6431 0.0945 0.8324 7.5320 0.0739 80-20% 0.6435 5.8306 0.0571 0.9061 7.5013 0.0752 1.1623 7.9102 0.0823 0.7902 6.4283 0.0615 90-10% 0.5327 4.9413 0.0428 0.8847 6.0415 0.0631 1.1434 7.5243 0.0731 0.6651 5.8291 0.0543 95-5% 0.3628 3.7805 0.0345 0.8520 5.8950 0.0580 1.0928 6.7840 0.0672 0.5923 5.0240 0.0461 98-2% 0.2596 3.2021 0.0302 0.8324 5.345 0.0532 1.0529 6.5741 0.0657 0.5616 4.6216 0.0462 N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 239
  6. 6. 2 inputs i.e. temperature, and dissolved oxygen concentration, whereas Penicillin concentration is the output. The data con- sists of 1580 samples out of which 1550 patterns are used for training the soft sensor and 30 for testing. Fig. 3(a) shows the estimated penicillin concentration by all the designed soft sensors. The MSE of prediction obtained from all the soft sensors are shown in Fig. 4. It is revealed from results that the prediction performance of ADALINE soft sensor is more effi- cient in comparison to other designed sensors, which moti- vates for its use in inferential control scheme as discussed in the next section. 4. Soft sensor based inferential control of bioreactor In any chemical process, the quality of final products is an es- sential parameter to be controlled. It is observed from open loop response of the model that concentration of ethanol takes large time (approximately 200hrs) to settle. The objective is to achieve the desired concentration as early as possible in the presence of disturbances and noise. Further the measurement of product quality using composition analyzers is a time delayed as well as uneconomic task. Therefore soft sensor based inferential mea- surements can be used in feedback configuration for process control. In pharmaceutical industries, food processing and bev- erages industries etc., inferential control is an increasingly used methodology that allows process quality to be inferred from sec- ondary measurements such as pressure, temperature, and flow etc. Benefits of inferential measurements are fast retrieval of in- formation, more consistent production as human involvement is reduced and process is optimized. The implementation of this method requires the selection of appropriate secondary variables. In this article inferential control scheme comprises of soft sensor, error detector and controller. As discussed earlier ADALINE soft (a) Predicted Ethanol Concentration (b) Error between target and predicted values 0 10 20 30 40 50 -3 -2 -1 0 1 2 3 No.of samples error ADALINE LMPNN BRBNN BPNN 0 10 20 30 40 50 11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 No. of samples Cp(ehanolconcentrationg/l) Desired Concentration ADALINE Predicted LMPNN Predicted BRBNN Predicted BPNN Predicted Fig. 1. (a) Predicted Ethanol Concentration. (b) Error between target and predicted values. (a) Predicted Ethanol Concentration for validation dataset (b) Error between target and predicted values 0 50 100 150 200 10.5 11 11.5 12 12.5 13 13.5 14 No. of samples )l/gnoitartencnoClonahtE(pC 80 90 100 110 120 11 12 13 14 Desired concentartion ADALINE LMPNN BRBNN BPNN 0 50 100 150 200 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 No. of samples Error ADALINE LMPNN BRBNN BPNN Fig. 2. (a) Predicted ethanol concentration for validation dataset. (b) Error between target and predicted values. Table 4 Quantitative analysis of soft sensors for validation samples. Soft sensor MSE MAPE MARE ADALINE 0.3098 3.4370 0.0344 LMPNN 0.8452 5.8060 0.0581 BRBNN 0.5723 4.7528 0.0475 BPNN 0.9252 5.9012 0.0610 N. Pachauri et al. / ISA Transactions 68 (2017) 235–250240
  7. 7. sensor provides accurate and fast measurements and therefore it is used for the control purpose. The training of soft sensor is per- formed offline with the help of simulation data obtained from the open loop simulation of continuous bioreactor. The trained soft sensor is then utilized in the closed loop control to provide the composition estimation from the temperature profile of the pro- cess. Error detector finds the error between reference and con- trolled variable whereas two types of controllers PID and 2-DOF- PID are designed to control final product quality of bioreactor. 4.1. PID control Traditional PID control scheme is widely employed in almost every process industry due to its simple structure and ease of implementation. The Laplace transform of PID controller is given by [39] ( ) = ( ) { + + } ( )M s E s K K s sK/ 18p i d Where E(s) is the error between reference and process output and Kp, Ki and Kd are proportional, integral and derivative gains of PID controller. The controller may be effectively used only if these gains are selected properly. The gains of the PID controller are designed on the basis of tracking error. The basic block diagram of PID controller is shown in Fig. 5.where ( )M s is the manipulating variable given to the process, ( )Y s is the output obtained from system and ( )R s is the reference input. PID controller takes the error between R(s) and Y(s) as input and it generates the suitable manipulated signal for the process in order to minimize the error between R(s) and Y(s). 4.2. Two degree of freedom PID (2-DOF-PID) Control The Laplace transform of classical PID controller (Eq. 18) is also termed as one degree of freedom PID which can give better per- formance for a single task i.e. to enhance the step response or for load disturbance rejection [40]. Two degree of freedom PID (2DOF- PID) is defined by the following equation. ⎛ ⎝ ⎜⎜ ⎞ ⎠ ⎟⎟ ( ) ( ) = ( ) ( ( ) − ( )) + ( ( ) − ( )) + ( ( ( ) − ( ))) ( + ) 19 M s E s K bR s Y s K s R s Y s K s cR s Y s sK K N1 / p i d d p The above equation can be rewritten as ( ) = ( ) ( ) − ( ) ( ) ( )M s R s L s Y s H s 20 Where ( ) = + + ( + ) ( ) H s K K s sK sK K N1 / 21 p i d d p (a) Predicted Penicillin Concentration for real time data set (b) Error between target and predicted values 15.5 16 16.5 17 34.75 34.8 34.85 34.9 34.95 35 0 5 10 15 20 25 30 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 No of Samples EstimatedError 0 5 10 15 20 25 30 34.6 34.8 35 35.2 35.4 35.6 35.8 36 No of Samples )L/g(noitartnecnoCnillicinePdetamitsE Fig. 3. (a) Predicted Penicillin Concentration for real time data set. (b) Error between target and predicted values. 0 0.005 0.01 0.015 ADALINE LMPNN BRBNN BPNN 7.62E-5 6.86E-4 6.26E-4 1.43E-2 MSE Fig. 4. MSE of estimation for all the soft sensors. Y(s)M(s) R(s) PID G(s) E(s) Fig. 5. Block diagram for PID controller in closed loop. N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 241
  8. 8. ( ) = + + ( + ) ( ) L s K b K s sK c sK K N1 / 22 p i d d p Where Kp,Ki, and Kd are proportional, integral and derivative gain respectively. ( )E s is the error signal, ( )M s is the control signal, ( )Y s is the process output (CP), ( )R s is the set point (CPset), N is derivative filter, c and b are the weights that affect the set point. The ( )L s and ( )H s functions not only maintain a desired output response but also provide a good regulatory performance. Fig. 6 shows the block diagram for 2-DOF-PID. Values of all the gains and derivative filter are same for L(s) and H(s) but appropriate values of b and c should be chosen in such a way that they do not interact with each other for efficient per- formance of the system. Two variants of soft sensors i.e. ADALINE and Retrained ADALINE (R-ADALINE) are used for inferential con- trol scheme of continuous bioreactor as discussed below. 4.3. ADALINE based inferential control scheme The chemical processes have large time delay, therefore the effect of temperature on the final concentration is detected after a large time. This causes delay in the control of desired ethanol concentration. Inferential control scheme reckoned in this article uses reactor temperature (Tr), and jacket temperature (Tag) to predict the product concentration. Thus value of concentration is anticipated and required manipulation is done before the product quality is actually affected. The proposed control scheme being anticipatory may lead to an efficient and fast control. The devel- oped ADALINE soft sensor is used to control ethanol production of continuous bioreactor inferentially. Schematic diagram for con- tinuous bioreactor with inferential controller is shown in Fig. 7. Eq. (6) shows that reactor temperature depends on temperature of cooling agent (Tag) which in turn depends on flow rate of cooling agent, therefore reactor temperature is controlled by manipulating the flow rate of cooling agent. As discussed in Section 3.1 the soft sensors are trained offline using 3450 samples and 50 samples are used for testing purpose. The trained sensor is used to measure the ethanol concentration in the feedback loop of bioreactor process. The ADALINE sensor uses Tr and Tag as input and infer the ethanol concentration online. The predicted concentration (CP) is compared with the desired con- centration (CPset) in the error detector to generate the error signal, which is further manipulated by various controllers. The ma- nipulated variable changes the position of actuator or control valve and hence the flow rate of cooling agent (Fag). Two types of con- trollers are used to control product concentration inferentially with ADALINE soft sensor i.e. PID (APID) and 2-DOF-PID (A2PID). The Tyreus-Luyben (TL) tuning method is used to tune all the gain parameters Kp, Ki and Kd of PID. The gain parameters Kp, Ki and Kd for 2-DOF-PID are first evaluated using conventional tuning method (TL) by considering N, b and c parameters values equal to 1, secondly the values of gain parameters are fixed while N, b and c parameters are varied. The variation of b and c affects the over- shoot and response of the system in transient state, whereas the variation in the value of N affects the susceptibility of noise in derivative action. The values of all governing parameters are given in Table 5. It is observed from Fig. 8 that ADALINE with 2-DOF-PID gives better control of ethanol concentration. The overshoot and settling time is significantly less for A2PID as compared to APID. The re- actor temperature has a great impact on other process variables and is thus a very important parameter for bioreactor. The in- ferential control scheme not only controls the final product con- centration but also optimally adjusts the reactor temperature and L(s) G(s) H(s) R(s) M(s) Y(s) D(s) Fig. 6. Block diagram for 2-DOF-PID controller. Error F , C , T Fe, Cs, CX , CP ,Tr ,Co2 Soft sensor CP,set Tr Tag CP,m PID / 2-DOF-PID Fag Inferential control scheme C = Cell Concentration C = Glucose Concentration C = Ethanol Concentration C = Dissolved oxygen concentration T = Reactor Temperature T = Jacket temperature F = Flow rate of cooling agent Manipulated variable, m pH =6 Fig. 7. Schematic diagram for inferential control of continuous bioreactor. N. Pachauri et al. / ISA Transactions 68 (2017) 235–250242
  9. 9. the yeast concentration according to the desired ethanol con- centration. The performance of ADALINE is further enhanced by continuous online training as discussed in the next section. 4.4. R-ADALINE based inferential control scheme ADALINE based inferential control scheme uses neural network as soft sensor. The neural network builds input-output relation- ship in terms of layer weights on the basis of training samples. If there is a sudden change in the process parameters due to some disturbance or external noise in the system, inputs of the sensor may deviate from the range of training samples, leading to errors in predictions. In order to overcome such problems, the neural network must be retrained with past measurements and latest information of system dynamics may be added instantaneously. The ADALINE trains fast as it is a single layered neural network and works efficiently with large training data, this capability may be utilized to make it adaptive towards parameter variations. Therefore ADALINE is retrained dynamically which leads to re- trained ADALINE soft sensor. The added information allows the soft sensor to adapt any change in the input variables of the pro- cess. In this inferential control scheme again PID and 2-DOF-PID are used for control of ethanol concentration with dynamic or retrained ADALINE which leads to two inferential controllers named as R-ADALINE with PID inferential controller (RAPID) and R-ADALINE with 2-DOF-PID (RA2PID). The PID and 2-DOF-PID controllers are tuned using the same method as in APID and A2PID controllers discussed previously. The tuning parameters of the two controllers are given in Table 6. Fig. 9(a) shows the ethanol concentration of bioreactor con- trolled by various inferential controllers. The R-ADALINE sensor can predict the concentration more accurately as its training data set is increased and thus any change in input process variable may be adapted efficiently and effectively. The mean square error of prediction for ADALINE is 0.2596 and for retrained ADALINE is 0.1096 which verifies the improvement in efficiency of prediction by R-ADALINE. It is observed from the Fig. 9 that the RAPID and RA2PID maintain the concentration at the set point more Table 5 Tuning parameters of controller for APID and A2PID. Controllers Kp Ki Kd N B c APID 20.85 0.856 220.88 – – – A2PID 17.28 0.826 228.99 4.486 0.9684 0.9947 (a) Ethanol concentration (b) Yeast concentration (c) Reactor temperature (d) Jacket temperature 0 100 200 300 400 500 600 700 800 900 1000 12.5 13 13.5 14 14.5 15 15.5 Time (hr) Ethanolconcentration-Cp(g/l) set point APID A2PID 0 100 200 300 400 500 600 700 800 900 1000 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.4 Time (hr) Yeastconcentration-Cx(g/l) APID A2PID 0 100 200 300 400 500 600 700 800 900 1000 29 30 31 32 33 34 35 36 Time (hr) ).itneceerged(rT-erutarepmeTrotcaeR APID A2PID 0 100 200 300 400 500 600 700 800 900 1000 27 28 29 30 31 32 33 34 35 36 Time (hr) ).itneceerged(gaT-erutarepmeTtekcaJ APID A2PID Fig. 8. Inferential control of continuous bioreactor by APID and A2PID. Table 6 Tuning parameters of controller for RAPID and RA2PID. Controllers Kp Ki Kd N b C RAPID 26.47 0.628 232.25 – – – RA2PID 14.079 0.429 142.83 3.862 0.935 0.8934 N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 243
  10. 10. accurately with minimum settling time and overshoot in com- parison to APID and A2PID controllers. Further the comparison also shows that introduction of 2-DOF-PID controller also im- proves the performance. This shows the capability of the 2-DOF- PID controller to maintain the desired profile in the presence of major or minor changes. Substrate concentration, reactor tem- perature and jacket temperature are also controlled efficiently which have great impact on ethanol production. Table 7 shows the performance indices comparison of all the controllers. From Table 7 it is revealed that the overshoot, settling time and other parameters are reduced significantly by the use of retrained ADALINE sensor along with 2-DOF-PID controller. Thus RA2PID controls the ethanol concentration in a better way in comparison to other designed controllers. It is also revealed that the perfor- mance indices of A2PID are better as compared to RAPID, the reason is that the parameters b and c in 2-DOF-PID simultaneously improve the transient and steady state performance of the system. Tyreus-Luyben method is used to tune the gain parameter for 2-DOF-PID and rigorous experimentation is done to calculate the optimum value of derivative filter N and two weight factors b and c. In order to improve the performance of RA2PID a multi-objec- tive optimization technique called Non sorted genetic algorithm-II (NASGA-II) is used to obtain the optimum values for proportional gain (Kp), Integral gain (Ki), derivative gain (Kd), derivative filter (N) and two weight factors b and c of 2-DOF-PID controller, which leads to R-ADALINE with NASGA –II tuned 2-DOF-PID controller i.e. RAN2PID. 4.4.1. Non-dominated sorting genetic algorithm-II Srinivas and Deb [41] proposed a modification to simple Ge- netic Algorithm (GA) called as Non-Dominated Sorting Genetic Algorithm (NSGA) for multi-objective optimization, which is suc- cessfully applied to different problems. But the main cause of criticism is its high computational complexity, lack of elitism and choice of optimal parameter values. Deb et al . [42] further sug- gested a non-dominated sorting-based multi-objective Evolu- tionary Algorithm (MOEA), called non-dominated sorting genetic algorithm II (NSGA-II), which overcomes all the above difficulties. In NSGA-II the population size N is initialized similar to GA. After initialization of population, all the individuals are arranged based on non-domination. Each individual or solution is assigned rank values equal to its non-domination level. Fitness value of 1 is given (a) Ethanol concentration (b) Yeast concentration (c) Reactor temperature (d) Jacket temperature 0 100 200 300 400 500 600 700 800 900 1000 12.5 13 13.5 14 14.5 15 15.5 Time (hr) Ethanolconcentration-Cp(g/l) set point APID RAPID A2PID RA2PID 0 100 200 300 400 500 600 700 800 900 1000 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.4 Time (hr) Yeastconcentration-Cx(g/l) APID RA2PID A2PID RA2PID 0 100 200 300 400 500 600 700 800 900 1000 29 30 31 32 33 34 35 36 Time (hr) ).itneceerged(rT-erutarepmeTrotcaeR APID RAPID A2PID RA2PID 0 100 200 300 400 500 600 700 800 900 1000 27 28 29 30 31 32 33 34 35 36 Time (hr) ).itneceerged(gaT-erutarepmeTtekcaJ APID RAPID A2PID RA2PID Fig. 9. RAPID and RA2PID based control for Ethanol concentration of bioreactor process. Table 7 Performance indices of designed controllers for ethanol concentration. Controllers % Overshoot Settling time (hr) Rise time (hr) Steady state error g/l APID 8.0839 104.4261 13.1512 0.2428 A2PID 5.8833 87.8710 12.8090 0.1303 RAPID 7.5443 103.6240 12.9389 0.0582 RA2PID 3.6452 71.6281 12.7130 0.0328 N. Pachauri et al. / ISA Transactions 68 (2017) 235–250244
  11. 11. to an individual whose non domination level is best and fitness value of 2 is assigned to the second non dominant individual and so on. A new factor called crowding distance is calculated for each individual apart from the fitness value. The crowding distance is a criterion based on the comparison of congestion around a solution and is used in the selection of parents for a new individual and the new population. A greater crowding distance is preferred in order to maintain the diversity of the solutions. Then a children popu- lation of size N is created by applying the genetic operator i.e. binary tournament selection based on the rank and crowding distance, recombination and mutation. The population of parents and children are reunited forming a temporary population of size 2N. The new population is again sorted according to non-Pareto front on the basis of rank and crowding distance method. The whole process is repeated until no front is accommodated. Gov- erning parameters of NSGA-II are given in Table 8. As discussed in Section 4.4, RA2PID inferential controller shows significantly better control performance in comparison to the other designed controllers. The gain parameters of 2-DOF-PID controller are obtained from Tyreus-Luyben tuning method whereas remaining parameters are obtained from rigorous ex- perimentation. There is still a scope of improvement in the per- formance of RA2PID which can be achieved by optimizing the parameters of 2-DOF-PID. The optimization problem minimizes two objective functions i.e. rise time and settling time. The lower and upper limit of decision variables for 2-DOF-PID are: ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤ ≤K K K N b12 18,0.1256 0.6781,138 146,1 5,0 1p i d ≤ ≤cand 0 1 Pareto-optimal sets of optimization problem after 30 generations are shown in Fig. 10 for rise time and settling time. The optimum values of NSGA-II tuned 2-DOF-PID are, Kp¼16.1695, Ki ¼ 0.4856, Kd¼142.714, N¼3.492, b¼0.904 and c¼0.926. The performance comparison of RAN2PID with other designed controllers for ethanol concentration control is given in Fig. 11. RAN2PID improves the transient as well as steady state performance of the process. The dissolved oxygen concentration is an important parameter which depends on reactor temperature (Tr). Eq. (8) shows the dissolved oxygen concentration in reaction medium with con- stant volume, which depends on two important variables i.e. equi- librium concentration of oxygen in the liquid phase ( *CO2 ) and mass transfer coefficient ( )k al . From Eq. (9) and Eq. (11) it is clearly seen that both the parameters vary according to the reactor temperature. The RAN2PID not only controls the ethanol concentration and re- actor temperature precisely but also controls the DO and other in- terlinked parameter effectively in minimum time. The improvement in performance of RAN2PID is verified from the quantitative analysis given in Table 9 in comparison to other designed inferential con- trollers. Thus it is obvious from the results that NSGA-II tuned 2-DOF-PID controller provides better performance for concentration control of bioreactor process. 4.5. Robustness analysis A controller must be robust enough to provide satisfactory control performance in the facets of disturbance and changes in set point. The designed controllers are tested for set point tracking and disturbance rejection in order to verify the robustness. 4.5.1. Set-point tracking In case of nonlinear process, change in set-point may com- pletely change the behavior of the process. The controller once tuned at some fixed operating point should not require the re- tuning for changes in set point. The robustness of controller may be tested by its application on some set points for which it is not tuned. Testing the controller for different set-points, also verifies the robustness of the controller to handle uncertainties in the process. For this purpose the designed controllers are tested for positive and negative changes in the set point. The ethanol con- centration is changed from 14 g/l to 15 g/l, 15 g/l to 13 g/l and 13 g/ l to 14 g/l at intervals of 300 h. It is observed from the results shown in Fig. 12 (a) that RAN2PID reduces the overshoot and settling time significantly in comparison to other designed con- trollers. In this case also the RAN2PID outperforms the other de- signed controllers. The corresponding variations of manipulating variable are shown in Fig. 12 (b) and found in acceptable range. The comparison of Integral square error (ISE) and Integral absolute error (IAE) for various designed controllers for entire time axis are shown in Fig. 13. It is obvious from the results that proposed in- ferential control scheme adapts the changes in the set-point quite efficiently. 4.5.2. Disturbance rejection The main problem with all chemical processes is the sudden change in input variable or generation of some external noise in the system which affects the output significantly. These external dis- turbances affect the final product of bioreactor. So controller must be robust which not only rejects the disturbance but at the same time maintains the product quality as well. As discussed earlier temperature is a very important and sensitive parameter for any chemical process and has a great impact on process operation. Therefore a 75% change in input temperature of feed is taken as disturbance for the process. Fig. 14 (a) and (b) shows the dis- turbance rejection capability of all the designed controllers for po- sitive and negative changes in the input temperature. Table 10 shows the quantitative analysis of all the controllers for disturbance rejection in terms of mean absolute error (MAE). It is revealed from the analysis that RAN2PID is the most efficient controller in re- jecting disturbances among the designed controllers. Disturbance may also be introduced into the system in the form of noise, hence random white noise is added to the process in the Table 8 Governing parameters of NSGA-II. Parameter Method and value Number of objective function 2 Number of design variables 6 Population size 20 Maximum Generation 30 Tournament pool size 2 Mutation method Polynomial Crossover method Simulated binary crossover (SBC) 55 60 65 70 75 80 8 10 12 14 16 18 20 Settling Time RiseTime Fig. 10. Pareto-optimal set of optimization between settling time and rise time. N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 245
  12. 12. feedback path (sensor noise) [18]. The amplitude of white noise considered varies from À1 to þ1, and its mean value is zero. The original tuning parameters of the APID, A2PID, RAPID, RA2PID and RAN2PID inferential controllers are kept unchanged. Fig. 15 (a) and (b) show the comparison of process variables and that of control outputs with noise, respectively. From Fig. 15 (a), it is found that when the same noise is added to two different set-points in the system, the control performance of RAN2PID is much better in comparison to the other designed controllers, as the concentration of ethanol is less oscillatory. Fig. 15 (b) reveals that controlled output of RAN2PID inferential control scheme is the most stable among designed controllers. The (a) Ethanol concentration (b) Yeast concentration (c) Reactor temperature (d) Jacket temperature 0 100 200 300 400 500 600 700 800 900 1000 12.5 13 13.5 14 14.5 15 15.5 Time (hr) Ethanolconcentration-Cp(g/l) set point APID RAPID A2PID RA2PID RAN2PID 0 100 200 300 400 500 600 700 800 900 1000 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.4 Time (hr) Yeastconcentration-Cx(g/l) APID RA2PID A2PID RA2PID RAN2PID 0 100 200 300 400 500 600 700 800 900 1000 29 30 31 32 33 34 35 36 Time (hr) ).itneceerged(rT-erutarepmeTrotcaeR APID RAPID A2PID RA2PID RAN2PID 0 100 200 300 400 500 600 700 800 900 1000 27 28 29 30 31 32 33 34 35 36 Time (hr) ).itneceerged(gaT-erutarepmeTtekcaJ APID RAPID A2PID RA2PID RAN2PID (e) Dissolved oxygen concentration 0 100 200 300 400 500 600 700 800 900 1000 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 Time (hr) )l/gm2oC(noitartncnocnegyxodevlossiD APID RAPID A2PID RA2PID RAN2PID Fig. 11. Comparison of RAN2PID with other designed controllers for Ethanol concentration. N. Pachauri et al. / ISA Transactions 68 (2017) 235–250246
  13. 13. results are further verified from ISE (Fig. 16) for noise suppression. Hence, the ability of noise suppression of the RAN2PID is much better than the other controllers. It is revealed from the above analysis that RAN2PID outper- forms the other designed controllers. This is due to the fact that this control scheme uses retrained ADALINE and optimized 2-DOF PID controller. The retrained ADALINE uses added information of immediate past so as to adapt the changes in the input variables. Further optimized two degree of freedom PID has two extra parameters b and c which enhance its performance. Thus RAN2PID proves to be an efficient and robust controller for control of con- tinuous bioreactor. 5. Discussion Bioreactors are highly nonlinear and time delayed systems and take several days (e.g. 5 days or 10 days) or hours (e.g. 200 h.) to reach the desired set point [43–45]. It is due to the fact that micro-organism takes time to grow, initially the output obtained is less but as the specific growth rate of biomass increases with temperature, the product concentration tends to the desired set point. The objective of control schemes is to regulate the system parameters (Tr, Tag), which highly affect the growth rate of micro- organism and provides the favorable environmental conditions to grow. Real time bioprocesses run continuously for long hours or days and desired output is obtained after a long time. From Ta- ble 9 it is observed that PID based inferential control schemes take more than 100 h to settle. This is the simulation study however when related to real time process, the settling time will be of the same order [44]. Therefore the proposed inferential control scheme, RAN2PID regulates the system parameters in such a way that desired ethanol concentration is achieved within 60 h or within 2.5 days. Sampling time is a very important issue in discrete time control systems. The value of sampling time is suitably selected so that number of samples generated per second should be enough for analysis. It directly affects the system re- sponse. If the samples are obtained at large time intervals then reconstructed signal gets distorted. In this work the Euler alge- braic solver is used with sampling time of 0.01 seconds. Further the trained soft sensor models the input-output relationship and therefore requires very less convergence time (0.143 s) for pre- diction of concentration. The rise time and settling time of the controller (12.2350, 60.4982) are the measures of speed of the controller response. Transients are generated in a system in the presence of varying inputs. The inputs vary during startup, sudden change in the value of input (set point change), external disturbance and noise. This transient behavior of the system lasts for a very short period if the controller is suitably designed. It is revealed from the results that proposed RAN2PID controller has lesser settling time i.e. transients die out earlier in contrast to other designed inferential control schemes. Further RAN2PID also shows acceptable transient per- formance during set point tracking and disturbance rejection. The presence of sensor noise disturbs the concentration profile and affects the transient performance of designed controllers. The proposed controller is able to reject the sensor noise of amplitude ranging from À0.8 to þ0.8. In case of larger amplitude noise the transient performance of all the controllers including the proposed one is degraded. Generally suitable filters are employed to reduce such sensor noise. The semi-rigorous mathematical model of bioreactor process considered for the present work does not represent the actual system completely because of modelling assumptions. As dis- cussed in Section 2 mathematical model facilitates the control engineer to get an insight about the behavior of controller in the Table 9 Performance indices comparison of RAN2PID with other designed controllers. Controllers % Overshoot Settling Time (hr) Rise time (hr) Steady state error (g/l) APID 8.0839 104.4261 13.1512 0.2428 A2PID 5.8833 87.8710 12.8090 0.1303 RAPID 7.5443 103.6240 12.9389 0.0582 RA2PID 3.6452 71.6281 12.7130 0.0328 RAN2PID 2.7432 60.4982 12.2350 0.0257 (a) Ethanol concentration (b) Controller’s output 0 100 200 300 400 500 600 700 800 900 1000 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 Time (hr) EthanolConcentrationCp(g/l) setpoint APID RAPID A2PID RA2PID RAN2PID 0 100 200 300 400 500 600 700 800 900 1000 0 20 40 60 80 100 120 140 Time (hr) ControllerOutput APID RAPID A2PID RA2PID RAN2PID Fig. 12. Set-point tracking comparison for all designed controllers. IAE ISE 0 100 200 300 400 APID A2PID RAPID RA2PID RAN2PID 330.91 260.24 285.01 224.93 210.13 289.46 232.12 249.68 199.75 184.21 IAE ISE Fig. 13. IAE and ISE analysis of set-point tracking for different controllers. N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 247
  14. 14. presence of disturbance, noise etc. without employing it on the real process. While designing the model of any real process few assumptions need to be considered. For instance, if the dy- namics of pH are included in the mathematical model, it will affect the dissolved oxygen concentration (Eqs. 8, 9, and 10) which further affects the ethanol concentration. Similarly if variable stirring speed is considered then due to non-uniform mixing, growth rate of micro-organism will be affected which consequently perturbs the final product concentration. There- fore previously tuned inferential controller will not be able to control ethanol concentration precisely. In order to improve the performance of inferential control scheme, the soft sensor needs to be retrained with the help of new dataset generated from modified bioreactor model and parameters of 2-DOF-PID con- troller must be re-tuned. 6. Conclusion The present work deals with soft sensor based concentration estimation and control of a continuous bioreactor. ADALINE based soft sensor is designed for the purpose and its prediction performance is found better than LMPNN, BRBNN, and BPNN. Two inferential control schemes based on ADALINE with PID (a) + 5% change in Tin (b) -5% change in Tin 0 100 200 300 400 500 600 700 800 900 1000 12.5 13 13.5 14 14.5 15 15.5 16 16.5 Time (hr) EthanolConcentrationCp(g/l) APID RAPID A2PID RA2PID RAN2PID 0 100 200 300 400 500 600 700 800 900 1000 12.5 13 13.5 14 14.5 15 Time (hr) EthanolConcentrationCp(g/l) APID RAPID A2PID RA2PID RAN2PID Fig. 14. Comparison of designed controllers for positive and negative disturbance rejection. Table 10 Quantitative analysis of all the controllers for disturbance rejection. Controllers þ5% change in Tin À5% change in Tin MAE MAE APID 0.5683 0.4571 A2PID 0.5432 0.4318 RAPID 0.5524 0.4382 RA2PID 0.5241 0.4334 RAN2PID 0.4306 0.3834 (a) Noise suppression by various controllers (b) Controller output 0 100 200 300 400 500 600 700 800 900 1000 12 12.5 13 13.5 14 14.5 15 15.5 16 Time (hr) EthanolConcentrationCp(g/l) setpoint APID RAPID A2PID RA2PID RAN2PID 0 100 200 300 400 500 600 700 800 900 1000 0 20 40 60 80 100 120 140 Time (hr) Controllersoutput APID RAPID A2PID RA2PID RAN2PID Fig. 15. (a) Noise suppression by various controllers. (b) Controller output. 0 100 200 300 APID A2PID RAPID RA2PID RAN2PID 290.42 245.16 252.76 237.33 223.14 ISE Fig. 16. Comparison of ISE for noise suppression. N. Pachauri et al. / ISA Transactions 68 (2017) 235–250248
  15. 15. (APID) and 2-DOF-PID (A2PID) controllers are suggested. It is revealed form the results that A2PID performs better than APID due to the additional controller parameters. Further the perfor- mance of ADALINE is improved by retraining it with past mea- surements. The retrained ADALINE used with PID and 2-DOF-PID leads to RAPID and RA2PID inferential control schemes. It is re- vealed form the results that RA2PID controls ethanol concentra- tion precisely with minimum overshoot and settling time. The performance of RA2PID inferential controller is further enhanced by optimizing the parameters of 2-DOF-PID with NSGA-II leading to RAN2PID controller. The control performance of RAN2PID controller is found better than other designed controllers for set- point tracking, disturbance rejection and noise suppression. Hence it is concluded from the results that retrained soft sensor along with optimized 2-DOF-PID controller provides robust and efficient control of bioreactor. Appendix A. Fundamental model parameters A1¼9.5 Â 108 (kla)0 ¼38 hÀ1 MMg¼24 g/mol A2¼2.55 Â 1033 KO2 ¼8.86 mg/l MMgCl2 ¼95 g/mol AT ¼1 m2 KP¼0.139 g/l MNa¼25 g/mol Cheat,ag ¼4.18 JgÀ1 KÀ1 KP1 ¼0.07 g/l MNaCl¼58.5 g/mol Cheat,r ¼4.18 JgÀ1 KÀ1 KS ¼ .03 g/l R¼8.31 J molÀ1 KÀ1 Ea1 ¼55,000 J/mol KS1¼1.68 g/l RSP ¼0.435 Ea2 ¼220,000 J/mol KT ¼3.6 Â 105 J hÀ1 mÀ2 KÀ1 RSX ¼0.607 HCa¼ À0.303 mCaCO3¼100 g YO2¼0.97 mg/mg HCl ¼0.844 mMgCl2 ¼100 g ΔHr ¼518 kJ/molO2 HCO3¼0.485 mNaCl¼500 g μO2 ¼.5 hÀ1 HH ¼ À0.774 MCa¼40 g/mol μP ¼1.79 hÀ1 HMg¼ À0.314 MCaCO3¼90 g/mol ⍴ag¼1000 g/l HNa¼ À0.550 MCl ¼35.5 g/mol ⍴r ¼1080 g/l HOH ¼0.941 MCO3¼60 g/mol Appendix B. Operating conditions of the process Parameter Description Values Fi Input Flow 51 l/h Fe Output Flow 51 l/h Tin Input flow temperature 25 °C Te Output flow temperature 25 °C Tin,ag Temperature of input cooling agent 15 °C Cs,in Concentration of glucose input flow 60 g/l kla Mass transfer coefficient for oxygen 38. (1024)Tr-20 V Total volume of reaction medium 1000 l Vj Volume of the jacket 50 l pH Potential of Hydrogen 6 Fag Flow rate of cooling agent 18 l/h References [1] Cheruy A. Software sensors in bioprocess engineering. J Biotechnol 1997;52:193–9. [2] Ödman P, Johansen CL, Olsson L, Gernaey KV, Lantz AE. On-line estimation of biomass, glucose and ethanol in Saccharomyces cerevisiae cultivations using in-situ multi-wavelength fluorescence and software sensors. J Biotechnol 2009;144:102–12. [3] Han HG, Qiao JF. Prediction of activated sludge bulking based on a self-orga- nizing RBF neural network. J Process Control 2012;22:1103–12. [4] Warth B, Rajkai G, Mandenius CF. Evaluation of software sensors for on-line estimation of culture conditions in an Escherichia coli cultivation expressing a recombinant protein. J Biotechnol 2010;147:37–45. [5] Chen LZ, Nguang SK, Li XM, Chen XD. Soft sensors for on-line biomass mea- surements. Bioprocess Biosyst Eng 2004;26:191–5. [6] Gustavsson R, Mandenius CF. Soft sensor control of metabolic fluxes in a re- combinant Escherichia coli fed-batch cultivation producing green fluorescence protein. Bioprocess Biosyst Eng 2013;36:1375–84. [7] Liu G, Zhou D, Xu H, Mei C. Model optimization of SVM for a fermentation soft sensor. Expert Syst Appl 2010;37:2708–13. [8] Tian Y, Zhang J, Morris J. Optimal control of a fed-batch bioreactor based upon an augmented recurrent neural network model. Neurocomputing 2002;48:919–36. [9] Veloso ACA, Rocha I, Ferreira EC. Monitoring of fed-batch E.coli fermentations with software sensors. Bioprocess Biosyst Eng 2009;32:381–8. [10] Vostiar I, Tkac J, Mandenius CF. Off-line monitoring of bacterial stress response during recombinant protein production using an optical biosensor. J Bio- technol 2004;111:191–201. [11] Ward AJ, Hobbs PJ, Holliman PJ, Jones DL. Evaluation of near infrared spec- troscopy and software sensor methods for determination of total alkalinity in anaerobic digesters. Bioresour Technol 2011;102:4083–90. [12] Sharma S, Tamb SS. Soft-sensor development for biochemical systems using genetic programming. Biochem Eng J 2014;85:89–100. [13] Sagmeister P, Kment M, Wechselberger P, Meitzb A, Langemann T, Herwiga C. Soft-sensor assisted dynamic investigation of mixed feed bioprocesses. Pro- cess Biochem 2013;48:1839–47. [14] Wechselberger P, Sagmeister P, Herwig C. Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes. Bioprocess Biosyst Eng 2013;36:1205–18. [15] Venu Vinod A, Arun Kumar K, Reddy G Venkat. Simulation of biodegradation process in a fluidized bed bioreactor using genetic algorithm trained feed- forward neural network. Biochem Eng J 2009;46:12–20. N. Pachauri et al. / ISA Transactions 68 (2017) 235–250 249
  16. 16. [16] Bravo MJA, Izquierdo JMC, Sanchez EG, Nieto MJL, Dimitriadis YA, Coronado JL. Automatization of a penicillin production process with soft sensors and an adaptive controller based on neuro fuzzy systems. Control Eng. Pract. 2004;12:1073–90. [17] Frickle J, Pohlmann K, Tatge F, Lang R, Faber B, Luttmann R. A multi-bioreactor system for optimal production of malaria vaccine with Pichia pastoris. Bio- technol J 2011;6:437–45. [18] Liu B, Ding Y, Gao N, Zhang X. A bio-system inspired nonlinear intelligent controller with application to bio-reactor system. Neurocomputing 2015;168:1065–75. [19] Nagy ZK. Model based control of a yeast fermentation bioreactor using opti- mally designed artificial neural networks. Chem Eng J 2007;127:95–109. [20] Imtiaz U, Assadzadeh A, Jamuar SS, Sahu JN. Bioreactor temperature controller using inverse neural network (INN) for production of ethanol. J process con- trol 2013;23:731–42. [21] Rani Asha, Singh Vijander, Gupta JRP. Development of soft sensor for neural network based control of distillation column. ISA Trans 2013;52:438–49. [22] Choe YS, Luyben WL. Rigorous dynamic model of distillation columns. ind Eng Chem Res 1987;26:2158–61. [23] Aiba S, Shoda M, Nagatani M. Kinetics of product inhibition in alcoholic fer- mentation. Biotechnol Bioeng 1968;10:846–64. [24] Yurish SY. Sensors: smart vs. Intelligent. Sens Transducers J 2009;29:98–103. [25] Singh V, Gupta I, Gupta HO. ANN based estimator for distillation using Le- venberg-Marquardt Approach. Eng Appl Artif Intell 2007;20:249–59. [26] de Assis AJ, Filho RM. Soft sensors development for on-line bioreactor state estimation. Comput Chem Eng 2000;24:1099–103. [27] MacKay DJC. Bayesian interpolation. Neural Comput 1992;4:415–47. [28] Ticknor JL. A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst Appl 2013;40:5501–6. [29] Foresee F Dan, Hagan Martin T. Gauss-newton approximation to bayesian learning. Proc Int Jt Conf Neural Netw 1997. [30] Hagan MT, Demuth HB, Beale M. Neural network design. 7th International Student Edition. Vikas Publishing House; 2003. [31] Huang GB. Learning capability and storage capacity of two-hidden-layer feedforward networks”. IEEE Trans Neural Netw 2003;14:274–81. [32] Huang GB, Babri H. General approximation theorem on feedforward networks, International conference on information, communications and signal proces- sing, ICICS ’97; 1997, 698–702. [33] V. Raghavan SR, Radhakrishnan TK, Srinivasan K. Soft sensor based composi- tion estimation and controller design for an ideal reactive distillation column. ISA Trans 2011;50:61–70 [2011]. [34] Kaneko H, Funatsu K. Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyper parameter settings and window size. Comput Chem Eng 2013;58:288–97. [35] Wu J, Long J, Liu M. Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 2015;148:136–42. [36] Bahar A, Ozgen C. State estimation and inferential control for a reactive batch distillation column. Eng Appl Artif Intell 2010;23:262–70. [37] 〈www.industrialpenicillinsimulation.com〉. [38] Goldrick S, Stefan A, Lovett D, Montague G, Lennox B. The development of an industrial-scale fed-batch fermentation simulation. J Biotechnol 2015;193:70–82. [39] Aström KJ, Hagglund T. The future of PID control. Control Eng Pract 2001;9:1163–75. [40] Vilanova R, Alfaro VM, Arrieta O. Analytical robust tuning approach for two degree-of-freedom PI/PID controllers. Eng Lett 2011;19:204–14. [41] Srinivas N, Deb K. Multiobjective function optimization using non dominated sorting genetic algorithms,. Evol Comput 1995;2:221–48. [42] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multi-objective genetic algorithm:NSGA-II. IEEE Trans Evolut Comput 2002;6:182–97. [43] Ozturk SS, Thrift JC, Blackie JD, Naveh D. Real-time monitoring and control of glucose and lactate concentrations in a mammalian cell perfusion reactor. Biotechnol Bioeng 1997;53:372–8. [44] Zhang H, Lennox B. Integrated condition monitoring and control of fed-batch fermentation processes. J Process Control 2004;14:41–50. [45] Chynoweth DP, Svoronos SA, Hyberatos G, Harman JL, Pullammanappallil P, Owens JM, Peck MJ. Real Time Expert system control of Anaerobic digestion. water Sci Technol 1994;30:21–9. N. Pachauri et al. / ISA Transactions 68 (2017) 235–250250

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