The multiple-reactor cascade operation is a distinctive characteristic in the process industry. However, it is difficult to establish an accurate and global model for multi-reactor cascading processes. Moreover, the intricate and dynamic operating state of the reactor, coupled with rear reactors, poses significant challenges to the fine control of the entire process. Therefore, this paper proposes a distributed intelligent control method based on state self-learning. Initially, the time-varying dynamic model of each reactor unit is established by learning the parameters of the regression model at each state point, achieving a nonlinear description of the reactor under complex conditions. Subsequently, leveraging the dynamic model and the material conservation principle between reactors, multi-step collaborative prediction is conducted along the reactor cascade direction. Thirdly, distributed model predictive control based on error self-correction is adopted to realize distributed intelligent control of the cascade reactor. This method is verified in a zinc smelting leaching process. The results indicate its superiority over common methods, offering higher prediction accuracy for the cascade process and enabling more effective control of individual reactors through distributed intelligence, which provides a novel and promising control paradigm for the cascade process.Note to Practitioners—The future heralds an era of the Internet of Everything, and this transformation extends to the production processes of the process industry. Presently, decentralized control methods are prevalent in the process industry. However, these methods lack communication between controllers and autonomy in learning. While decentralized control methods can effectively regulate most industrial processes, they struggle to achieve optimal control in scenarios with cascading reactors, where the reactors are interdependent. Distributed control methods offer promise to address these limitations. Regrettably, research and application of distributed control in process industry engineering remain limited, lacking suitable methods for controller autonomy and information exchange among different controllers. Consequently, this paper presents a novel control approach for stable and efficient regulation of multi-reactor cascades in the process industry, offering promising avenues for widespread application.

Abstract Online and accurate estimation of key performance indicators (KPI) is the foundation for operational optimization of a chemical process. However, a chemical process usually consists of multiple reactors, and the factors influencing KPI are spatially distributed in the long process flow. In addition, due to the distinct time lags between KPI and each reactor, temporal relationships among KPI and its influence factors are a mixture of short‐term and long‐term relationships. In this regard, a deep distributed KPI estimator with a self‐attention mechanism is proposed in this paper. First, considering the process topology, a cascaded long short‐term memory network is developed to simulate the process topology and capture the short‐term effects. Then, to extract the long‐term dependencies, a de‐noise self‐attention layer is employed to model interactions of all the influence factors explicitly and dynamically. Lastly, the proposed method is compared with typical state‐of‐the‐art methods using real industrial data. The comparison results illustrate the performance and effectiveness of the proposed KPI estimation method.

The roasting temperature is critical for enhancing product quality, reducing air pollution, and ensuring the long term operation of the zinc roasting process. However, optimizing the roasting temperature is challenging due to complex reaction mechanisms, feed composition fluctuations, and the coupling relationship with downstream processes. In this paper, a two level decision-making system for co-optimization of the roasting temperature is proposed. In the first level, a fuzzy synthetic evaluation model with variable-weight degradation degree is established to accurately evaluate the operating performance of the zinc roasting process. The evaluation results are used to design the basic setting rules that provide the basic temperature setting values. In the second level, a concept of temperature adjustable margin is introduced via sensitivity analysis of the pro cess model to evaluate the optimality of two roasters in the zinc roasting process. Based on the temperature-adjustable margin, the collaborative setting rules are designed to reasonably allocate the basic setting value to the two zinc roasters for optimizing the operating performance of the zinc roasting process. Finally, an industrial case study is presented to demonstrate the effectiveness of the proposed two-level decision-making system.