How can fine chemicals achieve intelligent production

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This post was last edited by ldby01 on 2025-10-29 at 19:13. Core concept: Process stability is the foundation of automation and intelligence. In fine chemicals, process stability is essential. Any upgrade to a control strategy must first ensure that no new, uncontrollable risks are introduced, while striving to improve the stability of the process and the consistency of the products. The fundamental goal of automation and intelligence is to maintain key process parameters (temperature, pressure, pH value, flow rate, components, etc.) within optimal values over longer production cycles, thereby ensuring product quality, increasing yields, and guaranteeing safety. Phase 1: Semi-automation. Key features: Automation of individual tasks, human-machine collaboration, with humans playing the leading role. Technical composition: Basic instruments and actuators: Pneumatic/electric combined instrument units are used for on-site monitoring (such as pressure gauges, glass thermometers) and simple control (such as self-acting pressure control valves). Relay logic control: Used to implement simple sequential logic controls, such as the start/stop of pumps and the interlocking of valve openings. Decentralized PID control loops: Some critical loops (such as the reactor temperature) use single-loop PID controllers, but many non-critical parameters still rely on manual monitoring and operation. From the perspective of process stability, the advantage is that compared to purely manual operations, semi-automation frees operators from repetitive and strenuous physical labor, reduces certain human errors, and provides a basic level of assurance for the stability of key parameters. Limitation: High dependence on operator experience – The adjustment of the process and the handling of abnormal conditions rely entirely on the operator’s judgment and manual intervention; differences in the operating habits of different teams can lead to fluctuations in product quality. “The phenomenon of \"information silos\" is severe: various control points operate independently of one another, lacking coordination, which makes it difficult to achieve coordinated optimization across multiple variables. Low stability ceiling: For complex processes with large delays, non-linearity, and strong coupling (such as batch reaction processes), simple PID control struggles to maintain long-term stability, leading to overshoot or oscillations. Phase 2: Fully automated. Key features: Automation of “lines” and “surfaces”, centralized monitoring, with machines playing a primary role. Technical composition: Distributed control system: This is the core of full automation. DCS enables centralized monitoring, operation, and management of all circuits and equipment in the entire production facility, while the control functions are distributed to the field control stations, ensuring high reliability. Safety Instrumented System: Independent of the DCS, it is dedicated to implementing safety interlock shutdown functions, serving as the \"bottom line\" for ensuring process stability and production safety. Sequential control and batch processing: Taking into account the intermittent/semi-interruption nature of production in the fine chemical industry, SFC or recipe management is used to program and automatically execute various steps such as material feeding, temperature raising, reaction, heat retention, and product discharge for an entire batch. From the perspective of process stability, there has been a tremendous improvement in consistency: batch processing ensures that the production process is strictly consistent for each batch, eliminating variations between batches caused by human factors. Global monitoring and coordination: DCS can establish complex control schemes (such as cascade, feedforward, and ratio control) to better handle the coupling relationships between variables, thereby improving stability under complex operating conditions. Enhanced safety barriers: The SIS system can respond to extreme conditions such as overheating and overpressure within milliseconds, actively preventing accidents and thus ensuring safe and stable operation over the long term. Challenges faced: “Static” optimization: Fully automated systems typically operate according to predefined, fixed logic and parameters. When the properties of the raw materials, catalyst activity, or environmental conditions change, the system is unable to adjust its set values autonomously to maintain optimal operating conditions. Dependent on accurate models: More advanced control strategies require an accurate mathematical model of the process plant, and fine chemical processes have complex mechanisms, making modeling difficult. Phase 3: Advanced Control Core feature: “Dynamic optimization,” which seeks the optimal operating point under multi-variable constraints. Technical composition: Model predictive control: the core technology of APC. MPC uses dynamic models to predict the changes in a process over an upcoming period, and through rolling optimization it determines a series of optimal control actions, while handling multi-input multi-output systems, large time lags, and constraint issues. Soft sensing technology: For key quality parameters that are difficult to measure online (such as product concentration and viscosity), real-time inference is carried out by establishing mathematical models with easily measurable variables (temperature, pressure, etc.), thereby providing feedback for APC. Real-time optimization: Typically situated above the APC layer, it calculates and sends the optimal process setpoints to the APC layer in real time, based on economic objectives (such as minimizing costs or maximizing output) and process models. From the perspective of process stability, this leads to an improvement in quality and enhanced resistance to disturbances: the predictive capabilities and multi-variable handling ability of MPC enable it to effectively overcome disruptions such as fluctuations in raw materials and changes in the environment, allowing process parameters to be maintained more closely around their set values and thus reducing fluctuations significantly. Operate stably within constraint boundaries: Fine chemical production often takes place near the boundaries of various operational constraints, such as maximum temperature and maximum pressure. APC can effortlessly drive the process to its optimal state without violating any constraints, ensuring both stability and cost efficiency. Overcoming non-linearity and large time lags: Particularly suitable for equipment such as fine chemical reactors and distillation columns that exhibit significant non-linearity and large time lag characteristics, overcoming the limitations of traditional PID control. Phase 4: Intelligent Control and Smart Factories – Core characteristics: \"Autonomous sensing, decision-making, and evolution,\" enabling predictive maintenance and coordinated optimization across the entire process. Technical composition: Industrial Internet of Things and digital twins: Enables the collection and integration of data from all relevant elements through a large number of high-precision sensors and wireless networks. Digital twins create a virtual model that is synchronized in real time with the physical factory, for simulation, analysis, and prediction. Big Data Analysis and Artificial Intelligence: Machine Learning/Deep Learning: Uncovering deeper patterns in large volumes of historical and real-time data to create data-driven models that are more accurate than traditional mechanistic models, for use in quality prediction, fault diagnosis, and more. Reinforcement learning*: By enabling the agent to learn optimal control strategies through continuous interaction with the environment, it achieves adaptive optimization that goes beyond APC. Intelligent control algorithms: Combine expert knowledge, fuzzy logic, neural networks, and other techniques with traditional control methods to handle extremely complex processes whose mechanisms are not well understood and that experience rapid changes. From the perspective of process stability, the ultimate goals are resilience, adaptability, and predictability. Predictive maintenance: By analyzing data such as equipment vibration and temperature, AI can predict failures in key devices like pumps and compressors in advance, allowing for repairs to be carried out before unplanned shutdowns occur, thereby ensuring the continuity and stability of production. Root cause quality control: By performing correlation analysis on data from the entire process, intelligent systems can identify the underlying causes of fluctuations in the final product quality (is it a certain batch of raw material?) Is it the status of a certain device? ), and provides adjustment suggestions to achieve a transition from \"controlling parameters\" to \"controlling quality\". Adaptability: When the catalyst gradually loses its activity or the performance of the equipment declines slowly, the system is able to detect this slow-time variation on its own, and adjust the control strategies and process parameters to maintain optimal production conditions at all times. End-to-end collaborative optimization: Intelligent factories break down the barriers between production units, enabling intelligent decision-making across the entire process—from orders and scheduling to energy management—so that the whole factory can operate stably and efficiently as a unified entity. Summary and Outlook: The development of fine chemicals from semi-automation to intelligence is a process of continuous expansion of the control loop: In semi-automation, only a single variable is controlled. Fully automated; controls individual units/production lines. Advanced control optimizes multiple interrelated variables. Intelligence coordinates the entire production ecosystem of \"people, machines, materials, methods, environment, and monitoring\". Process stability has always been the guiding principle and value criterion throughout this evolution process. Progress at each stage implies achieving a higher level of process stability in more complex environments over longer periods of time, ultimately leading to the excellent operation sought after in the fine chemical industry: 100% safety, 100% quality consistency, and maximum economic efficiency. For China’s fine chemical enterprises, recognizing this development path, building upon their existing process foundations, and gradually advancing automation and intelligent upgrades is the only way to achieve high-quality development.