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Articles 1 - 7 of 7
Full-Text Articles in Engineering
Process Modeling And Optimization Strategies, Sandip K. Lahiri
Process Modeling And Optimization Strategies, Sandip K. Lahiri
Dr. Sandip Kumar Lahiri
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 …
Prediction Of Pressure Drop Of Slurry Flow In Pipeline By Hybrid, Sandip K. Lahiri
Prediction Of Pressure Drop Of Slurry Flow In Pipeline By Hybrid, Sandip K. Lahiri
Dr. Sandip Kumar Lahiri
This paper describes a robust support vector regression (SVR) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique (SVR-GA) for efficient tuning of SVR meta-parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters
Process Modeling And Optimization Of Mono Ethylene Glycol Quality In Commercial Plant Integrating Artificial Neural Network And Differential Evolution, Sandip K. Lahiri
Process Modeling And Optimization Of Mono Ethylene Glycol Quality In Commercial Plant Integrating Artificial Neural Network And Differential Evolution, Sandip K. Lahiri
Dr. Sandip Kumar Lahiri
This paper presents an artificial intelligence based process modeling and optimization strategies, namely artificial neural network – differential evolution (ANN-DE) for modeling and optimization of ultraviolet (UV) transmittance of mono ethylene glycol (MEG) product. UV transmittance is one of the most important quality variable of MEG that has impact on the polyester product quality. UV transmittance measures the presence of undesirable compounds in MEG that absorb light in the ultraviolet region of the spectrum and indirectly measures the purity of MEG product. They are in trace quantities in the ppb ranges and primarily unknown in chemical structure. Thus, they cannot …
Computational Fluid Dynamics Simulation Of The Solid Liquid Slurry Flow In A Pipeline, Sandip K. Lahiri
Computational Fluid Dynamics Simulation Of The Solid Liquid Slurry Flow In A Pipeline, Sandip K. Lahiri
Dr. Sandip Kumar Lahiri
A comprehensive computational fluid dynamics (CFD) model was developed in the present study to gain insight into the concentration profile of solid liquid slurry flow in pipelines. The preliminary simulations highlighted the need for the correct modeling of the interphase drag force. In light of the shortcomings of appropriate drag co-efficient model (namely Syamlal O’-Brien model, Gidaspow model and Wen & Yu model), an effort was made in the present study to modify the existing models to incorporate the effect of solid concentration at drag co-efficient. A two-dimensional user defined model was then developed using CFD to understand the influence …
The Support Vector Regression With The Parameter Tuning Assisted By A Differential Evolution Technique: Study Of The Critical Velocity Of A Slurry Flow In A Pipeline, Sandip K. Lahiri
Dr. Sandip Kumar Lahiri
This paper describes a robust Support Vector regression (SVR) methodology, which can offer a superior performance for important process engineering problems. The method incorporates hybrid support vector regression and a differential evolution technique (SVR-DE) for the efficient tuning of SVR meta parameters. The algorithm has been applied for the prediction of critical velocity of the solid-liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved the prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters.
Fault Diagnosis For Large Complex Petrochemical Plant, Sandip K. Lahiri
Fault Diagnosis For Large Complex Petrochemical Plant, Sandip K. Lahiri
Dr. Sandip Kumar Lahiri
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 …
Development Of An Artificial Neural Network Correlation For Prediction Of Hold-Up Of Slurry Transport In Pipelines, Sandip K. Lahiri
Development Of An Artificial Neural Network Correlation For Prediction Of Hold-Up Of Slurry Transport In Pipelines, Sandip K. Lahiri
Dr. Sandip Kumar Lahiri
In the literature, very few correlations have been proposed for hold-up prediction in slurry pipelines. However, these correlations fail to predict hold-up over a wide range of conditions. Based on a databank of around 220 measurements collected from the open literature, a correlation for hold-up was derived using artificial neural network (ANN) modeling. The hold-up for slurry was found to be a function of nine parameters such as solids concentration, particle dia, slurry velocity, pressure drop and solid and liquid properties. Statistical analysis showed that the proposed correlation has an average absolute relative error (AARE) of 2.5% and a standard …