- Response surface methodology (RSM) uses statistical techniques to model and analyze problems with response variables influenced by multiple independent variables. The goal is to optimize the response.
- RSM has been used since the 1930s and was reviewed in landmark papers in 1966 and 1976. It is commonly used in industries, agriculture, medicine, and other fields to optimize processes and products.
- There are two main experimental strategies in RSM - first-order models to initially evaluate relationships between factors and responses, and second-order models to account for curvature and find optimal points if curvature is present.
you can know about the central composite design, historical design, optimisation techniques and also about the TYPES OF CENTRAL COMPOSITE DESIGN, BOX-BEHNKEN DESIGN, DATA COLLECTION, CRITICISM OF DATA, PRESENTATION OF FACTS, PURPOSE, OPTIMISATION PROCESS, DIFFERENT TYPES PRESENT IN IT AND THEIR CLASSIFICATION AND EXPLANATION.
Optimization techniques in formulation Development Response surface methodol...D.R. Chandravanshi
The term “optimize” is “to make as perfect”. It is defined as follows: choosing the best element from some set of variable alternatives.
An art ,process ,or methodology of making something (a design system or decision ) as perfect ,as functional, as effective as possible .
you can know about the central composite design, historical design, optimisation techniques and also about the TYPES OF CENTRAL COMPOSITE DESIGN, BOX-BEHNKEN DESIGN, DATA COLLECTION, CRITICISM OF DATA, PRESENTATION OF FACTS, PURPOSE, OPTIMISATION PROCESS, DIFFERENT TYPES PRESENT IN IT AND THEIR CLASSIFICATION AND EXPLANATION.
Optimization techniques in formulation Development Response surface methodol...D.R. Chandravanshi
The term “optimize” is “to make as perfect”. It is defined as follows: choosing the best element from some set of variable alternatives.
An art ,process ,or methodology of making something (a design system or decision ) as perfect ,as functional, as effective as possible .
Approaches to Experimentation
What is Design of Experiments
Definition of DOE
Why DOE
History of DOE
Basic DOE Example
Factors, Levels, Responses
General Model of Process or System
Interaction, Randomization, Blocking, Replication
Experiment Design Process
Types of DOE
One factorial
Two factorial
Fractional factorial
Screening experiments
Calculation of Alias
DOE Selection Guide
Introduction & Basics of DoE
Terminologies
Key steps in DOE
Softwares used for DOE
Factorial Designs ( Full and Fractional)
Mixture Designs
Response Surface Methodology
Central Composite Design
Box -Behnken Design
Conclusion
References
DESIGN OF EXPERIMENTS (DOE)
DOE is invented by Sir Ronald Fisher in 1920’s and 1930’s.
The following designs of experiments will be usually followed:
Completely randomised design(CRD)
Randomised complete block design(RCBD)
Latin square design(LSD)
Factorial design or experiment
Confounding
Split and strip plot design
FACTORIAL DESIGN
When a several factors are investigated simultaneously in a single experiment such experiments are known as factorial experiments. Though it is not an experimental design, indeed any of the designs may be used for factorial experiments.
For example, the yield of a product depends on the particular type of synthetic substance used and also on the type of chemical used.
ADVANTAGES OF FACTORIAL DESIGN.
Factorial experiments are advantageous to study the combined effect of two or more factors simultaneously and analyze their interrelationships. Such factorial experiments are economic in nature and provide a lot of relevant information about the phenomenon under study. It also increases the efficiency of the experiment.
It is an advantageous because a wide range of factor combination are used. This will give us an idea to predict about what will happen when two or more factors are used in combination.
DISADVANTAGES
It is disadvantageous because the execution of the experiment and the statistical analysis becomes more complex when several treatments combinations or factors are involved simultaneously.
It is also disadvantageous in cases where may not be interested in certain treatment combinations but we are forced to include them in the experiment. This will lead to wastage of time and also the experimental material.
2(square) FACTORIAL EXPERIMENT
A special set of factorial experiment consist of experiments in which all factors have 2 levels such experiments are referred to generally as 2n factorials.
If there are four factors each at two levels the experiment is known as 2x2x2x2 or 24 factorial experiment. On the other hand if there are 2 factors each with 3 levels the experiment is known as 3x3 or 32 factorial experiment. In general if there are n factors each with p levels then it is known as pn factorial experiment.
The calculation of the sum of squares is as follows:
Correction factor (CF) = (𝐺𝑇)2/𝑛
GT = grand total
n = total no of observations
Total sum of squares = ∑▒〖𝑥2−𝐶𝐹〗
Replication sum of squares (RSS) = ((𝑅1)2+(𝑅2)2+…+(𝑅𝑛)2)/𝑛 - CF
Or
1/𝑛 ∑▒𝑅2−𝐶𝐹
2(Cube) FACTORIAL DESIGN
In this type of design, one independent variable has 2 levels, and the other independent variable has 3 levels.
Estimating the effect:
In a factorial design the main effect of an independent variable is its overall effect averaged across all other independent variable.
Effect of a factor A is the average of the runs where A is at the high level minus the average of the runs
S1 - Process product optimization using design experiments and response surfa...CAChemE
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
Selecting experimental variables for response surface modelingSeppo Karrila
Basic common sense design of experiments starts from qualitative modeling, selecting factors to eliminate, and factors to adjust/control in the experimental design. The goal is to introduce sci & tech students to this approach, and to basics of response surface methods. Not math or statistics, a soft tutorial.
Approaches to Experimentation
What is Design of Experiments
Definition of DOE
Why DOE
History of DOE
Basic DOE Example
Factors, Levels, Responses
General Model of Process or System
Interaction, Randomization, Blocking, Replication
Experiment Design Process
Types of DOE
One factorial
Two factorial
Fractional factorial
Screening experiments
Calculation of Alias
DOE Selection Guide
Introduction & Basics of DoE
Terminologies
Key steps in DOE
Softwares used for DOE
Factorial Designs ( Full and Fractional)
Mixture Designs
Response Surface Methodology
Central Composite Design
Box -Behnken Design
Conclusion
References
DESIGN OF EXPERIMENTS (DOE)
DOE is invented by Sir Ronald Fisher in 1920’s and 1930’s.
The following designs of experiments will be usually followed:
Completely randomised design(CRD)
Randomised complete block design(RCBD)
Latin square design(LSD)
Factorial design or experiment
Confounding
Split and strip plot design
FACTORIAL DESIGN
When a several factors are investigated simultaneously in a single experiment such experiments are known as factorial experiments. Though it is not an experimental design, indeed any of the designs may be used for factorial experiments.
For example, the yield of a product depends on the particular type of synthetic substance used and also on the type of chemical used.
ADVANTAGES OF FACTORIAL DESIGN.
Factorial experiments are advantageous to study the combined effect of two or more factors simultaneously and analyze their interrelationships. Such factorial experiments are economic in nature and provide a lot of relevant information about the phenomenon under study. It also increases the efficiency of the experiment.
It is an advantageous because a wide range of factor combination are used. This will give us an idea to predict about what will happen when two or more factors are used in combination.
DISADVANTAGES
It is disadvantageous because the execution of the experiment and the statistical analysis becomes more complex when several treatments combinations or factors are involved simultaneously.
It is also disadvantageous in cases where may not be interested in certain treatment combinations but we are forced to include them in the experiment. This will lead to wastage of time and also the experimental material.
2(square) FACTORIAL EXPERIMENT
A special set of factorial experiment consist of experiments in which all factors have 2 levels such experiments are referred to generally as 2n factorials.
If there are four factors each at two levels the experiment is known as 2x2x2x2 or 24 factorial experiment. On the other hand if there are 2 factors each with 3 levels the experiment is known as 3x3 or 32 factorial experiment. In general if there are n factors each with p levels then it is known as pn factorial experiment.
The calculation of the sum of squares is as follows:
Correction factor (CF) = (𝐺𝑇)2/𝑛
GT = grand total
n = total no of observations
Total sum of squares = ∑▒〖𝑥2−𝐶𝐹〗
Replication sum of squares (RSS) = ((𝑅1)2+(𝑅2)2+…+(𝑅𝑛)2)/𝑛 - CF
Or
1/𝑛 ∑▒𝑅2−𝐶𝐹
2(Cube) FACTORIAL DESIGN
In this type of design, one independent variable has 2 levels, and the other independent variable has 3 levels.
Estimating the effect:
In a factorial design the main effect of an independent variable is its overall effect averaged across all other independent variable.
Effect of a factor A is the average of the runs where A is at the high level minus the average of the runs
S1 - Process product optimization using design experiments and response surfa...CAChemE
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
Selecting experimental variables for response surface modelingSeppo Karrila
Basic common sense design of experiments starts from qualitative modeling, selecting factors to eliminate, and factors to adjust/control in the experimental design. The goal is to introduce sci & tech students to this approach, and to basics of response surface methods. Not math or statistics, a soft tutorial.
The project was a study based report on the RAN evolution path of 2.5G EDGE Networks to HSDPA. HSDPA is a 3.5G wireless cellular system, a cost-efficient upgrade to UMTS systems and promises to deliver performance comparable to today’s wireless LAN services, but with the added benefit of mobility and ubiquitous coverage. It can offer data rates of up to 14.4 Mbps which is far beyond what 2.5G and 3G cellular systems could offer. The project focuses on a two-step upgrade, first from GSM towards the deployment of UMTS/WCDMA and then towards HSDPA. It begins a new era of “Mobile broadband” services and faces competition from “WiMAX” – but with GSM services having an obvious upgrade path to WCDMA, HSDPA seems to be leading the market in several parts of the world. HSDPA is an extremely cost-effective path to higher data rates and provides more efficient use of valuable spectrum. It enables operators to compete effectively in increasingly converged markets and satisfy the need for enhanced QoS in an efficient and cost-effective manner.
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Mixture Regression Model for Incomplete DataLoc Nguyen
The Regression Expectation Maximization (REM) algorithm, which is a variant of Expectation Maximization (EM) algorithm, uses parallelly a long regression model and many short regression models to solve the problem of incomplete data. Long regression model is entire regression function which is the resulted model and short regression models are partial regression functions which are inverses of entire regression function. I proposed REM in a different research in which an entire regression function is built parallelly with many partial inverse regression functions and then missing values are fulfilled by expectations relevant to both entire regression function and inverse regression functions. Experimental results proved resistance of REM to incomplete data, but accuracy of REM decreases insignificantly when data sample is made sparse with loss ratios up to 80%.
Like traditional regression analysis methods, accuracy of REM can be decreased if data varies complicatedly with many trends. In this research, I propose a so-called Mixture Regression Expectation Maximization (MREM) algorithm. MREM is full combination of REM and mixture model in which I use two EM processes in the same loop. MREM uses the first EM process for exponential family of probability distributions to estimate missing values as REM does. Consequently, MREM uses the second EM process to estimate parameters as mixture model method does. The purpose of MREM is to take advantages of both REM and mixture model. Unexpectedly, experimental result shows that MREM is less accurate than REM. I try to weight partial models of MREM by product of component probabilities and conditional probabilities or to select most appropriate partial model in order to improve estimation accuracy, but the final results are not as good as expected. However, MREM is essential because a different approach for mixture model can be referred by fusing linear equations of MREM into a unique curve equation proposed by some other researches.
Comparison of Several Numerical Algorithms with the use of Predictor and Corr...ijtsrd
In this paper, we introduce various numerical methods for the solutions of ordinary differential equations and its application. We consider the Taylor series, Runge Kutta, Euler's methods problem to solve the Adam's Predictor, Corrector and Milne's Predictor, Corrector to get the exact solution and the approximate solution. Subhashini | Srividhya. B "Comparison of Several Numerical Algorithms with the use of Predictor and Corrector for solving ode" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29318.pdf Paper URL: https://www.ijtsrd.com/mathemetics/other/29318/comparison-of-several-numerical-algorithms-with-the-use-of-predictor-and-corrector-for-solving-ode/subhashini
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Hello everyone! I am thrilled to present my latest portfolio on LinkedIn, marking the culmination of my architectural journey thus far. Over the span of five years, I've been fortunate to acquire a wealth of knowledge under the guidance of esteemed professors and industry mentors. From rigorous academic pursuits to practical engagements, each experience has contributed to my growth and refinement as an architecture student. This portfolio not only showcases my projects but also underscores my attention to detail and to innovative architecture as a profession.
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It is beyond a moot point that a good book will somewhat be judged by its cover, but the content of the book remains king. No matter how beautiful the cover, if the quality of writing or presentation is off, that will be a reason for readers not to come back to the book or recommend it.
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3. History
• In the Mead and Pike paper, they move back the
origin of RSM to include use of "response curves”
dating back into the 1930's.
• Then in 1935 Yates work on it.
• In the Hill And Hunter Review, they State that
• In November 1966, a paper “A Review of Response
Surface Methodology” ; A literature was published
by “Hill and Hunter. Its purpose was to review the
practical applications of RSM in chemical and related
fields.
4. History
• In December 1976, another paper “A Review of
Response Surface Methodology From A Biometric
View Point” was published by Mead and Pike
appeared.
• With the passage of time many Statisticians work on
RSM for Improvement.
5. Introduction
• Response surface methodology (RSM) uses various
statistical, graphical, and mathematical techniques to
develop, improve, or optimize a process, also use for
modeling and analysis of problems if our response
variables in influenced by several independent variables.
• Main objectives are as follow.
– Optimize.(main objective)
– Develop.
– Improve. (if necessary).
6. Real Life Examples
• RSM is used in different fields of real life. Like
Industries, Agriculture, Electronics, Medical field and
many other like this. It is use where we want to get
optimum response.
7. An Example of Medical field.
• A single tablet is introduced in market after large
number of experiments. Suppose a company want to
introduce a new pain killer tablet in market. The
pharmacist will make the table that will be more
effective and has rapid action to kill the pain, will
have low price at market for patient ………
9. Experimental Strategy
1. RSM resolve around the assumption that the
response is a function of a set of
independent(design) variables x1,x2,x3….xk and
function can be approximated in some region of
polynomial model.
𝑦 = 𝑓 𝑥𝑖
𝑦 = 𝑓 𝑥1, 𝑥2 … … 𝑥 𝑘
Here response variable is “y” that depend on the “k”
independent variables.
10. Experimental Strategy
2. If the factors are given then directly estimate the
effects and interaction of model as describe in figure.
3. And if the factors are unknown then first calculate
them by using the Screening method.
4. Estimate The Interaction effect using 1st order
model.
y = 𝛽0+𝛽1 𝑥1+𝛽2 𝑥2+𝜀
11. Experimental Strategy
5. If curvature is found then use the RSM. And 2nd
order model will be used to approximate the
response variable.
12. 6. Make the graph and find the stationary point.
Maximum response, Minimum response or saddle point
by using the obtained values of 𝑥1, 𝑥2, 𝑥3 … . 𝑥4.
13. Types OF Models
We use two types of model in RSM.
1. 1st Order Model.
2. 2nd Order Model.
When Use Which Model
• 1St Order Model.
Oftenly in RSM the relationship between response
variable and Independent variables is not given. After
screening we use 1st order model to find current
situation and to find either there is curvature or not.
y = 𝛽0+𝛽1 𝑥1+𝛽2 𝑥2+𝜀
14. 2nd Order Model
If we have find curvature after making fig from the
result of 1st order model.
Then we use 2nd order model to find our optimum point.
16. Sequential Nature Of RSM
RSM is sequential procedure. Often, when we are at a
point on the response surface that is remote from the
optimum, and we want to move rapidly from current
point to the optimum point with sequence.
If we want the optimum point where the sources are
minimum but output is maximum then that is called our
optimum point. And we move rapidly toward it.
17. (II) Methods of RSM
• There are two methods of RSM to obtain optimum
response. And we move toward our optimum point
with these two method..
» Method Of Steepest Ascent.
» Method Of Steepest Descent.
18. Steepest Ascent Method:
This is a procedure for moving sequentially in the
direction of the maximum increase in the response
getting optimum response.
Steepest Descent Method :
If minimization is desired then we call this
technique the “method of steepest descent”.
19. Steepest Ascent Method
• The initial estimate of the optimum operating
condition for this will be far from the actual optimum.
• In such circumstances, the objective of the
experimenter is to move rapidly to the general
vicinity(nearest point) of the optimum. We wish to
use a simple and economically efficient experimental
procedure. When we remote from the optimum, we
usually assume that a 1st order model is an adequate
approximation to the true surface in a small region of
the x’s.
20. Steepest Ascent Method
This is a procedure for
moving sequentially in
the direction of the
maximum increase in
the response getting
optimum response.
𝑦 = 𝛽𝑜 +
𝑖=1
𝑘
𝛽𝑖 𝑥𝑖
21. Steepest Descent Method
If minimization is
desired then we call this
technique the “method
of steepest descent”.
𝑦 = 𝛽𝑜 +
𝑖=1
𝑘
𝛽𝑖 𝑥𝑖
24. A first order model may be fit to these data by least,
employing the methods for two level designs, we obtain
the following model in the coded variable
𝑦 = 40.44 + 0.775𝑥1 + 0.775𝑥2
Before exploring along the path of steepest ascent, the
adequacy of the first order model should be investigated.
The 2^2 design with center points allows the experiment
to
1. Obtain an estimate of error
2. Check for interactions (cross product terms) in the
model
25. • the replicates at the center can be used to
calculate an
𝜎2 =
(40.3)2+(40.5)2+ 40.7 2+ 40.2 2+ 40.6 2−
(202.3)2
5
4
= 0.0430
The first order model assume that the variable 𝑥1 &
𝑥2 have an additive effect on the response.
Interaction b/w the variables would be represent by
the coefficient 𝛽12 of a cross product term 𝑥1 𝑥2
added to the model. the least square estimate of this
coefficient is just one half the interaction effect
calculated as in an ordinary 22 factorial design. Or
26. • 𝛽12 =
1
4
1 ∗ 39.3 + 1 ∗ 41.5 + −1 ∗ 40.0 + (−1 ∗ 40.9)
= -0.025
The single degree of freedom sum of square for
interaction is
SS interaction =
(−0.1)2
4
=0.0025
Comparing SS interactions to 𝜎2
gives a lack of fit
statistics
F =
𝑠𝑠 𝑖𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛
𝜎2 = 0.0025/0.0430 = 0.058
Which is a small ,indicating that interaction is
negligible
27. Application
The most frequent applications of RSM are in the
industrial area.
RSM is important in designing formulating and
developing and analyzing new specific scientific
studying and product.
It is also efficient in improvements of existing studies
and products
Most common application of RSM are in industrial
,biological and clinical sciences, social sciences ,food
sciences and physical and engineering sciences