Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Abstract

Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.

Numerical Algorithms in III–V Semiconductor Heterostructures

“…The PSO method utilizes two vectors: the current position of the particles, denoted as − → p , and the associated velocity, denoted as − → u . The method was used in many scientific problems from areas such as physics [46,47], chemistry [48,49], medicine [50,51], economics [52], etc. Also, the PSO method was used with success in neural network training [53,54].…”

Multi-stage manufacturing process parameter optimization method based on improved marine predator algorithm

“…However, the interpretability of the BP neural network methods mentioned above is poor, typically considered as a black-box model, making it difficult to explain the decision-making process and internal representations of the model, thereby increasing the difficulty of parameter optimization. Fang et al [10] aiming to optimize the industrial propane dehydrogenation process, achieved optimization objectives by combining machine learning methods and particle swarm optimization algorithms to solve optimization problems. However, particle swarm optimization algorithms suffer from poor local search capabilities, insufficient search accuracy, and complex parameter settings.…”

Toward an Ideal Particle Swarm Optimizer for Multidimensional Functions

“…Various researchers have provided an analytic review of the PSO process such as the review of Jain et al [35] where 52 papers have been reviewed, the work of Khare et al [36] where a systematic review of the PSO algorithm is provided along with the application of PSO in solar photovoltaic systems. The method was successfully used in a variety of scientific and practical problems in areas such as physics [37,38], chemistry [39,40], medicine [41,42], economics [43], etc. Many researchers have proposed a variety of modifications in the PSO method during the last few years; such methods aimed to estimate a more efficient calculation for the inertia parameter ω of the speed calculation [44][45][46].…”