Optimization of a Thermal Cracking Reactor Using Genetic Algorithm and Water Cycle Algorithm

[Image: see text] With the global production of 150 million tons in 2016, ethylene is one of the most significant building blocks in today’s chemical industry. Most ethylene is now produced in cracking furnaces by thermal cracking of fossil feedstocks with steam. This process consumes around 8% of the main energy used in the petrochemical industry, making it the single most energy-intensive process in the chemical industry. This paper studies a tubular thermal cracking reactor fed by propane and the molecular mechanism of the reaction within the reactor. After developing the reaction model, the existing issues, such as the reaction, flow, momentum, and energy, were resolved by applying heat to the outer tube wall. After solving the entropy generation equations, the entropy generation ratio of the sources was evaluated. The temperature of the tube/reactor was tuned following the reference results, and processes were replicated for different states. The verification of the modeling and simulation results was compared with the industrial case. The Genetic Programming (GP) machine learning approach was employed to generate objective functions based on key decision variables to reduce the computational time of the optimization algorithm. For the first time, this study has proposed a systematic approach for optimizing a thermal cracking reactor based on a combination of Genetic Programming (GP), Water Cycle Algorithm (WCA), and Genetic Algorithm (GA). In this regard, multiobjective optimization was performed based on the maximization of the products and entropy generation with the generation of GP objective functions. The key decision variables in this study included inlet gas temperature, inlet gas pressure, air mass flow rate, and wall temperature. The results showed that the weighted percentage of products after optimization increased to 61.13% and the entropy production rate of the system decreased to 899.80 J/s, displaying an improvement of 0.85 and 16.51% compared with the base case, respectively, with the multiobjective GA algorithm. In addition, by applying the multiobjective WCA, the weighted percentage of products increased to 61.81%. The entropy production rate of the system decreased to 882.72 J/s. So, an improvement of 1.97% in weights of products and an improvement of 18.77% in entropy generation have been achieved compared with the base case.