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• W4289883886 abstract "Membrane-based water and wastewater treatment have been identified as the key technology employed in chemical engineering. This technology has good ability for industrial practices owing to its cost-effectiveness, eco-friendly nature, and simplicity for upscaling. A precise mathematical model for these processes can be beneficial to foresee the performance of the separation. Typical optimization techniques are accomplished by differing one of the operational factors for a time. This is extremely time-consuming, expensive, and cannot confirm whether the optimal condition has been identified. Thus, a general technique is employed to fit a polynomial regression model for the operational factors and to execute numerical multi-factor optimization for the developed model.One of the flexible tools, response surface methodology (RSM), is employed to design experiments, empirical model development, and multi-factor optimization. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) are used for modeling and simulation. ANN is based on computational means to mimic the processing power of a biological brain. It can model and simulate remarkably sophisticated non-linear systems. But ANFIS blends the abilities of both a fuzzy inference system (FIS) and an adaptive neural network. They can explain hidden logic by their computational power of neural networks and fuzzy interface capabilities. The crucial step following model development is the optimization of operational factors. Optimization technique is an important step that makes substantial progress for the performance of the membrane process. Hence, in this chapter, crucial factors for the membrane-based process and integrating conventional RSM, ANN and ANFIS models with genetic algorithm (GA) to compare and evaluate the performance of orthodox models and by integrating non-conventional GA to model optimization." @default.
• W4289883886 created "2022-08-05" @default.
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• W4289883886 date "2022-01-01" @default.
• W4289883886 modified "2023-10-14" @default.
• W4289883886 title "Parameter optimization and modelling of forward osmosis membrane separation process" @default.
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• W4289883886 doi "https://doi.org/10.1016/b978-0-323-90627-2.00012-5" @default.
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