This paper proposes an AI-assisted approach aimed at accelerating chemical process design through causal incremental reinforcement learning (CIRL) where an intelligent agent is interacting iteratively with a process simulation environment (e.g., Aspen HYSYS, DWSIM, etc.). The proposed approach is based on an incremental learnable optimizer capable of guiding multi-objective optimization towards optimal design variable configurations, depending on several factors including the problem complexity, selected RL algorithm and hyperparameters tuning. One advantage of this approach is that the agent-simulator interaction significantly reduces the vast search space of design variables, leading to an accelerated and optimized design process. This is a generic causal approach that enables the exploration of new process configurations and provides actionable insights to designers to improve not only the process design but also the design process across various applications. The approach was valid... [more]

The overarching goal of limiting the increase in global temperature to ≤ 2.0˚ C likely requires both decarbonization and defossilization efforts. Direct air capture (DAC) and CO2 electrolysis stand out as promising technologies for capturing and utilizing atmospheric CO2. In this effort, we explore the details of designing and operating an integrated DAC-electrolysis process by examining some key parameters for economic feasibility. We evaluate the gross profit and net income to find the most appropriate capacity factor, average electricity price, syngas sale price, and CO2 taxes. Additionally, we study an optimistic scenario of CO2 electrolysis and perform a sensitivity analysis of the CO2 capture price to elucidate the impact of design decisions on the economic feasibility. Our findings underscore the necessity of design improvements of the CO2 electrolysis and DAC processes to achieve reasonable capacity factor and average electricity price limits. Notably, CO2 taxes and tax credits... [more]