Optimization

Process optimization plays a crucial role in process and system transformation; in the manufacturing process, for instance, it increases efficiency and reduces production costs through process improvement. In machine learning, on the other hand, optimization is used to improve the accuracy of a machine learning model by minimizing the degree of error and, hence, enhancing their learning to make accurate predictions. Furthermore, as we saw in previous chapters, data analytics and machine learning are fundamental to redefining process prediction and control, which will ultimately align with optimization. In this Chapter, we introduce simple optimization algorithms used for process engineers, such as grid search, random search, and gradient search, followed by a different generation of techniques, including evolutionary algorithms, particle swarm, and Bayesian inference and optimization. Moreover, we explore multi-objective optimization, a must tool engineers employ to improve decision-making in solving design and monitoring problems.