Energy optimization and neural-based dynamic analysis of integrated multiple stage evaporator

Abstract Minimizing energy consumption is often a grave challenge in many industrial energy-intensive process units such as Multiple Stage Evaporator (MSE). Integration of Energy Reduction Schemes (ERSs) is a common technique to resume a countable amount of energy. Hence, the present work proposes a hybrid (h-) ERSs integrated MSE placed in the paper industry, used to increase the solid content of the black liquor. The h-ERSs Integrated MSE comprises several ERSs such as Thermo-Vapor Compressor, Steam-, Feed-split, and Feed Preheater to improve the energy efficiency significantly, and its energy performance is compared with base (b-) MSE. For this purpose, nonlinear mathematical models have been developed and transformed into a constrained optimization problem to search for the optimum energy efficiency obtained as Steam Economy (SE). A state-of-art metaheuristic approach, Equilibrium Optimizer (EO), along with some well-acquainted solution approaches (Interior Point OPTimizer, Interior Point Method, and Particle Swarm Optimization) has been simulated in different platforms to estimate the maximum SE to check their competitiveness for this industrial optimization problem. It is observed that EO outperformed all the algorithms with a 66 % higher SE for h-MSE than b-MSE. Eventually, the steady state parameters are applied as the initial conditions to analyze the nonlinear enthalpy dynamics of the b-and h-MSE. A neural base solution has been adopted to rigorously study the open-loop process dynamics that meet the desired product quality.