This article provides a systematic review of recent progress in optimization-based process synthesis. First, we discuss multiscale modeling frameworks featuring targeting approaches, phenomena-based modeling, unit operation–based modeling, and hybrid modeling. Next, we present the expanded scope of process synthesis objectives, highlighting the considerations of sustainability and operability to assure cost-competitive production in an increasingly dynamic market with growing environmental awareness. Then, we review advances in optimization algorithms and tools, including emerging machine learning–and quantum computing–assisted approaches. We conclude by summarizing the advances in and perspectives for process synthesis strategies.

As the low-carbon economy continues to advance, New Energy Vehicles (NEVs) have risen to prominence in the automotive industry. The design and utilization of lithium-ion batteries (LIBs), which are core component of NEVs, are directly related to the safety and range performance of electric vehicles. The requirements for a refined design of lithium-ion battery electrode structures and the intelligent adjustment of charging modes have attracted extensive research from both academia and industry. LIB models can be divided into mechanism-based models and data-driven models; however, the distinctions and connections between these two kinds of models have not been systematically reviewed as yet. Therefore, this work provides an overview and perspectives on LIB modeling from both mechanism-based and data-driven perspectives. Meanwhile, the potential fusion modeling frameworks including mechanism information and a data-driven method are also summarized. An introduction to LIB modeling technologies is presented, along with the current challenges and opportunities. From the mechanism-based perspective of LIB structure design, we further explore how electrode morphology and aging-related side reactions impact battery performance. Furthermore, within the realm of battery operation, the utilization of data-driven models that leverage machine learning techniques to estimate battery health status is investigated. The bottlenecks for the design, state estimation, and operational optimization of LIBs and potential prospects for mechanism-data hybrid modeling are highlighted at the end. This work is expected to assist researchers and engineers in uncovering the potential value of mechanism information and operation data, thereby facilitating the intelligent transformation of the lithium-ion battery industry towards energy conservation and efficiency enhancement.

This paper reviews the recent advancements of reinforcement learning (RL) for chemical process control. RL presents a systematic strategy in which the machine learning agent learns a policy of actions based on interactions with the environment. We first provide a brief overview of RL theoretic basis built on Markov decision processes (MDPs) and then move onto its application to process control. With particular interest in chemical processes, we review state-of-the-art research developments on RL for controller tuning and direct control policy learning. This work highlights the importance of safe RL control to incorporate deterministic or probabilistic safety constraints such as constrained MDPs, control barrier functions, etc. We conclude the review with a discussion on some of the outstanding challenges such as sampling efficiency, generalizability, uncertainty, and observability, as well as the emergent and future directions to address these limitations.