AI for Chemical Process Industry

 There are two major types of modeling approaches in chemical engineering, namely, mechanistic (white box, first principle) and AI-based approach like ANN and fuzzy logic methods. In the mechanistic approach, fundamental physical and chemical laws, such as conservation laws, construct the basis of the model. This approach contains algebraic and differential equations which involve mass, energy and momentum balances. Due to the large number of variables affecting the process behavior and complex mathematical equations governing the system, many chemical processes are nonlinear and complicated. Consequently, it is hard and sometimes even impossible to present them by mechanistic models. Even if such a model has been developed, it might be impractical to solve or identify its parameters. Moreover, a mechanistic model needs detailed knowledge and a lot of skill and ingenuity to incorporate the basic phenomena of the process in the model.

In some cases, considering some assumptions such as physical properties’ constancy, ideality of gas phase and linearization of the nonlinear equations of the model is inevitable, which all impose limitations on the model leading to the reduction of the model’s robustness.

On the contrary, AI-based techniques have demonstrated their ability and have received much attention for chemical process modeling. These techniques, for which developing detailed knowledge of the process is of less concern, may overcome the drawbacks of the mechanistic approach when dealing with complex and nonlinear systems. 

AI algorithms can analyze real-time data from sensors and make adjustments to optimize various parameters such as temperature, pressure, and flow rates. This not only increases production efficiency but also reduces energy consumption and waste generation.