(BQ) Part 2 book "New optimization techniques in engineering" has contents: Applications in reaction engineering; new ideas and applications of scatter search and path relinking; improvement of search process in genetic algorithms - an application of PCB assembly sequencing problem; scheduling and production & control | 12 Applications in Reaction Engineering B V Babu Introduction This chapter presents the application of Genetic Algorithms (GA) & Differential Evolution (DE) on two most important chemical engineering problems: (1) Optimal design of an auto-thermal ammonia synthesis reactor, and (2) Optimization of thermal cracking operation. In the first problem, the Differential Evolution (DE), an evolutionary computation technique, is applied to the optimal design of an auto-thermal ammonia synthesis reactor. This paper also presents the new concept of “Nested” DE (DE is also used to find out the best combination of key parameters of DE itself). The main objective in the optimal design of an auto-thermal ammonia synthesis reactor is the estimation of the optimal length of reactor for different top temperatures with the constraints of energy and mass balance of reaction and feed gas temperature & mass flow rate of nitrogen for ammonia production. Thousands of combinations of feed gas temperature, nitrogen mass flow rate, reacting gas temperature and reactor length are possible. This section also presents the application of four methods, viz., Euler’s method, Runge-Kutta method (both variable & constant step size), & Gear’s method in combination with DE, and compare the results reported using GA in earlier literature. A software package “POLYMATH” is also used to solve the three equality constraints . three coupled differential equations. Apart from determining the optimal reactor length, the comparison of results obtained from different methods is presented. DE found to be a robust, fast and simple evolutionary computation technique for optimization problems. The second paper presents the application of Differential Evolution (DE) for the optimization of Thermal Cracking operation. The objective in this problem is the estimation of optimal flow rates of different feeds to the cracking furnace under the restriction on ethylene and propylene production. Thousands of .
