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This book presents a theoretical analysis of the modern methods used for modeling various chemical engineering processes. Currently, the two primary problems in the chemical industry are the optimal design of new devices and the optimal control of active processes. Both of these problems are often solved by developing new methods of modeling. These methods for modeling specific processes may be different, but in all cases, they bring the mathematical description closer to the real processes by using appropriate experimental data. In this book, the authors detail a new approach for the modeling of chemical processes in column apparatuses. Further, they describe the types of neural networks that have been shown to be effective in solving important chemical engineering problems. Readers are also presented with mathematical models of integrated bioethanol supply chains (IBSC) that achieve improved economic and environmental sustainability.
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