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Sandip Lahiri

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Papers by Sandip Lahiri

Research paper thumbnail of Development of an artificial neural network correlation for prediction of overall gas holdup in bubble column reactors

Chemical Engineering and Processing: Process Intensification, 2003

Research paper thumbnail of Profit Maximization Techniques for Operating Chemical Plants

Research paper thumbnail of Modeling and optimization of phycoremediation of heavy metals from simulated ash pond water through robust hybrid artificial intelligence approach

Research paper thumbnail of Simulated Annealing Technique to

Research paper thumbnail of Modeling of Commercial Ethylene Oxide Reactor: A Hybrid Approach by Artificial Neural Network & Differential Evolution

International Journal of Chemical Reactor Engineering, 2010

This study is motivated by a growing popularity of artificial neural network for process modeling... more This study is motivated by a growing popularity of artificial neural network for process modeling and regression problems. However, many ANN regression application studies are performed by ‘expert' users who have a good understanding of ANN methodology. Since the quality of ANN models depends on a proper setting of ANN architecture and ANN meta-parameters, the main issue for practitioners trying to apply ANN regression is how to set these parameter values (to ensure good generalization performance) for a given data set. Non-expert users face a difficulty in finding an optimum ANN architecture and are often confused about how to choose the ANN meta parameters. The present paper addresses this issue and develops a new hybrid procedure to find the optimum ANN architecture and tunes the ANN parameters, thus relieving the ‘non expert' users. This method incorporates hybrid artificial neural network and differential evolution technique (ANN-DE) for efficient tuning of ANN meta par...

Research paper thumbnail of Process Modeling and Optimization Strategies Integrating Support Vector Regression and Differential Evolution: A Study of Industrial Ethylene Oxide Reactor

Chemical Product and Process Modeling, 2008

This paper presents artificial intelligence-based process modeling and optimization strategies, n... more This paper presents artificial intelligence-based process modeling and optimization strategies, namely, support vector regression – differential evolution (SVR-DE) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR-DE approach, a support vector regression model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Differential Evolution (DE) with a view to maximize the process performance. DE possesses certain unique advantages over the commonly used gradient-based deterministic optimization algorithms. The SVR-DE is a new strategy for chemical process modeling and optimization. The major advantage of the strategy is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Us...

Research paper thumbnail of Design of distillation column by hybrid differential evolution and ant colony optimization technique

Research paper thumbnail of Multivariable Predictive Control

Research paper thumbnail of CHEMCON, 2008. Fault Diagnosis for Large Complex Petrochemical Plant

Often it is time consuming to monitor the plant condition in modern complex process industries as... more Often it is time consuming to monitor the plant condition in modern complex process industries as there is abundance of instrumentation that measure thousands of process variables in every few seconds. This has caused a "data overload" and due to the lack of appropriate analyses very little is currently being done to utilize this wealth of information. Fortunately, in process, groups of variables often moves together because more than one variable may be measuring the same driving principle governing the behavior of the process. Multivariate statistical methods such as Principal Component Analysis (PCA) are capable of compressing the information down into low dimensional spaces which retain most of the information. Using this method of statistical data compression a multivariate monitoring procedure has been developed to efficiently monitor the performance of large processes and to rapidly detect and identify important process changes. A graphical interface was developed w...

Research paper thumbnail of Performance enhancement of commercial ethylene oxide reactor by artificial intelligence approach

International Journal of Chemical Reactor Engineering

The present work emphasizes the development of a generic methodology that addresses the core issu... more The present work emphasizes the development of a generic methodology that addresses the core issue of any running chemical plant, i.e., how to maintain a delicate balance between profit and environmental impact. Here, an ethylene oxide (EO) production plant has been taken as a case study. The production of EO takes place in a multiphase catalytic reactor, the reliable first principle-based model of which is still not available in the literature. Artificial neural network (ANN) was therefore applied to develop a data-driven model of the complex reactor with the help of actual industrial data. The model successfully built up a correlation between the catalyst selectivity and temperature with other operational parameters. A hybrid multi-objective metaheuristic optimization technique, namely ANN-multi-objective genetic algorithm (MOGA) algorithm was used to develop a Pareto diagram of selectivity versus reactor temperature. The Pareto diagram will help the plant engineers to make a stra...

Research paper thumbnail of Optimization of High Density Polyethylene (HDPE) Reactor Using Artificial Intelligence

Interdisciplinary Research in Technology and Management

Research paper thumbnail of Artificial Intelligence based Modelling and Multi-Objective Optimization of Commercial Ethylene Oxide Reactor

Interdisciplinary Research in Technology and Management

Research paper thumbnail of Application of Artificial Neural Network and Particle Swarm Optimization for modelling and optimization of biosorption of Lead(II) and Nickel(II) from wastewater using dead cyanobacterial biomass

Journal of the Indian Chemical Society

Research paper thumbnail of Assessing the correlation between fatty acid composition of biodiesel with the fuel property using artificial intelligence and optimization

Environmental Progress & Sustainable Energy

Research paper thumbnail of Reduce Distillation Column Cost by Hybrid Particle Swarm and Ant Colony Optimization Technique

Journal of Chemical Engineering Research Updates

Research paper thumbnail of Modeling and optimization of cooking process parameters to improve the nutritional profile of fried fish by robust hybrid artificial intelligence approach

Journal of Food Process Engineering

Research paper thumbnail of Minimize power consumption in slurry transport : Accurately predict critical velocity

Hydrocarbon Processing, 2008

Research paper thumbnail of System and Method for Monitoring a Process

Research paper thumbnail of My PhD project synopsis

Research paper thumbnail of Computational fluid dynamics simulation of solid-liquid slurry flow

Hydrocarbon Processing, 2009

Research paper thumbnail of Development of an artificial neural network correlation for prediction of overall gas holdup in bubble column reactors

Chemical Engineering and Processing: Process Intensification, 2003

Research paper thumbnail of Profit Maximization Techniques for Operating Chemical Plants

Research paper thumbnail of Modeling and optimization of phycoremediation of heavy metals from simulated ash pond water through robust hybrid artificial intelligence approach

Research paper thumbnail of Simulated Annealing Technique to

Research paper thumbnail of Modeling of Commercial Ethylene Oxide Reactor: A Hybrid Approach by Artificial Neural Network & Differential Evolution

International Journal of Chemical Reactor Engineering, 2010

This study is motivated by a growing popularity of artificial neural network for process modeling... more This study is motivated by a growing popularity of artificial neural network for process modeling and regression problems. However, many ANN regression application studies are performed by ‘expert' users who have a good understanding of ANN methodology. Since the quality of ANN models depends on a proper setting of ANN architecture and ANN meta-parameters, the main issue for practitioners trying to apply ANN regression is how to set these parameter values (to ensure good generalization performance) for a given data set. Non-expert users face a difficulty in finding an optimum ANN architecture and are often confused about how to choose the ANN meta parameters. The present paper addresses this issue and develops a new hybrid procedure to find the optimum ANN architecture and tunes the ANN parameters, thus relieving the ‘non expert' users. This method incorporates hybrid artificial neural network and differential evolution technique (ANN-DE) for efficient tuning of ANN meta par...

Research paper thumbnail of Process Modeling and Optimization Strategies Integrating Support Vector Regression and Differential Evolution: A Study of Industrial Ethylene Oxide Reactor

Chemical Product and Process Modeling, 2008

This paper presents artificial intelligence-based process modeling and optimization strategies, n... more This paper presents artificial intelligence-based process modeling and optimization strategies, namely, support vector regression – differential evolution (SVR-DE) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR-DE approach, a support vector regression model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Differential Evolution (DE) with a view to maximize the process performance. DE possesses certain unique advantages over the commonly used gradient-based deterministic optimization algorithms. The SVR-DE is a new strategy for chemical process modeling and optimization. The major advantage of the strategy is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Us...

Research paper thumbnail of Design of distillation column by hybrid differential evolution and ant colony optimization technique

Research paper thumbnail of Multivariable Predictive Control

Research paper thumbnail of CHEMCON, 2008. Fault Diagnosis for Large Complex Petrochemical Plant

Often it is time consuming to monitor the plant condition in modern complex process industries as... more Often it is time consuming to monitor the plant condition in modern complex process industries as there is abundance of instrumentation that measure thousands of process variables in every few seconds. This has caused a "data overload" and due to the lack of appropriate analyses very little is currently being done to utilize this wealth of information. Fortunately, in process, groups of variables often moves together because more than one variable may be measuring the same driving principle governing the behavior of the process. Multivariate statistical methods such as Principal Component Analysis (PCA) are capable of compressing the information down into low dimensional spaces which retain most of the information. Using this method of statistical data compression a multivariate monitoring procedure has been developed to efficiently monitor the performance of large processes and to rapidly detect and identify important process changes. A graphical interface was developed w...

Research paper thumbnail of Performance enhancement of commercial ethylene oxide reactor by artificial intelligence approach

International Journal of Chemical Reactor Engineering

The present work emphasizes the development of a generic methodology that addresses the core issu... more The present work emphasizes the development of a generic methodology that addresses the core issue of any running chemical plant, i.e., how to maintain a delicate balance between profit and environmental impact. Here, an ethylene oxide (EO) production plant has been taken as a case study. The production of EO takes place in a multiphase catalytic reactor, the reliable first principle-based model of which is still not available in the literature. Artificial neural network (ANN) was therefore applied to develop a data-driven model of the complex reactor with the help of actual industrial data. The model successfully built up a correlation between the catalyst selectivity and temperature with other operational parameters. A hybrid multi-objective metaheuristic optimization technique, namely ANN-multi-objective genetic algorithm (MOGA) algorithm was used to develop a Pareto diagram of selectivity versus reactor temperature. The Pareto diagram will help the plant engineers to make a stra...

Research paper thumbnail of Optimization of High Density Polyethylene (HDPE) Reactor Using Artificial Intelligence

Interdisciplinary Research in Technology and Management

Research paper thumbnail of Artificial Intelligence based Modelling and Multi-Objective Optimization of Commercial Ethylene Oxide Reactor

Interdisciplinary Research in Technology and Management

Research paper thumbnail of Application of Artificial Neural Network and Particle Swarm Optimization for modelling and optimization of biosorption of Lead(II) and Nickel(II) from wastewater using dead cyanobacterial biomass

Journal of the Indian Chemical Society

Research paper thumbnail of Assessing the correlation between fatty acid composition of biodiesel with the fuel property using artificial intelligence and optimization

Environmental Progress & Sustainable Energy

Research paper thumbnail of Reduce Distillation Column Cost by Hybrid Particle Swarm and Ant Colony Optimization Technique

Journal of Chemical Engineering Research Updates

Research paper thumbnail of Modeling and optimization of cooking process parameters to improve the nutritional profile of fried fish by robust hybrid artificial intelligence approach

Journal of Food Process Engineering

Research paper thumbnail of Minimize power consumption in slurry transport : Accurately predict critical velocity

Hydrocarbon Processing, 2008

Research paper thumbnail of System and Method for Monitoring a Process

Research paper thumbnail of My PhD project synopsis

Research paper thumbnail of Computational fluid dynamics simulation of solid-liquid slurry flow

Hydrocarbon Processing, 2009

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