AI-Based Optimization and Multi-Mode Production in Cement Manufacturing

In the move towards smart manufacturing, the cement industry is actively embracing automation. The automation of production lines forms a key pillar in the development of intelligent cement plants, and the level of automation has a direct impact on overall plant efficiency. Currently, all of the approximate 1,600 cement production lines in china are equipped with Distributed Control Systems (DCS). While about 15% of these lines also use expert control systems, fewer than 1% have integrated AI-based decision-making systems. DCS handles simple control loops, expert systems manage more complex processes, and AI-driven systems optimize the control values for these expert systems, enabling operations to follow specific production modes such as energy conservation, high-output, or profit-maximization, all while maintaining operational stability and economic efficiency.

Introduction

Traditional cement production processes rely on DCS for control, where operators continuously adjust production settings based on real-time process data and quality demands to maintain product standards. As the industry transitions to smarter systems, some companies have begun implementing expert control systems for key operations such as raw material grinding, coal milling, clinker burning, and cement grinding, which helps stabilize process parameters and ensures product consistency.

However, both traditional and expert control systems share a common challenge: the key operational targets, such as decomposition furnace temperature, system pressure, and feed rates, are typically set based on operator experience. Once these targets are defined, automatic control or expert systems manage other variables such as valve adjustments, speed controls, and feed rates. While experienced operators can set reasonable targets, less experienced ones may cause process instability and quality fluctuations. Furthermore, these systems focus primarily on local control without sufficiently considering broader economic performance, which can lead to stable but suboptimal economic outcomes.

we focuses on optimizing the cement production process through AI-driven decision-making, aligning operational targets with economic objectives by analyzing production data and adjusting key control parameters for optimal plant-wide performance.

2. Significance of AI Decision-Making
2.1 Value of AI in Production

AI-driven decision-making systems derive optimal control targets by analyzing process data, equipment performance, and production metrics. These systems create production models for different operational scenarios and use real-time data to determine the best settings for each situation. By providing expert systems with more accurate targets, AI minimizes the need for operator intervention, leading to safer, more stable, energy-efficient, and higher-output production. This approach helps lay the foundation for future intelligent manufacturing, driving workforce optimization and organizational restructuring, thereby improving overall productivity.

2.2 Limitations of Traditional DCS

In both traditional DCS and expert control systems, operators typically set control targets based on experience, making it difficult for less-experienced personnel to manage efficiently. Furthermore, in cement production, variations in raw material and fuel composition lead to system instability, which current systems are not equipped to handle automatically. Manual target-setting also hampers automation and limits the potential for optimization.

Expert systems improve upon traditional PID control by considering a broader range of factors and incorporating operator experience. For example, in adjusting coal feed rates, the system accounts for different operational conditions, and control models, such as those for decomposition furnace temperature, are more effective than traditional PID methods. However, even with these improvements, the target values themselves are still operator-defined, which introduces variability based on the operator’s judgment.

In a smart cement plant, production control should automatically adapt to the type of cement being produced and the associated quality requirements. For instance, during periods of high demand, the system could switch to a high-output mode to maximize production. Alternatively, during times of high energy costs, it could switch to an energy-saving mode, aiming to minimize energy consumption.

2.3Multi-Mode Production and AI Decision Systems
    2.3.1 System Overview

    The architecture of an AI decision-making system for multi-mode production includes the following layers:

    Mode Selection Layer: This layer allows the selection of production modes based on economic objectives. It defines targets for key parameters like feed rates, energy consumption, and production stability, and provides forecasts for output quality and energy use.

    Real-Time Optimization (RTO) Layer: This layer continuously learns and updates operational models, based on the company’s objectives, such as maximizing production or minimizing energy usage. It calculates optimal values for key control parameters and passes these values to the Advanced Process Control (APC) layer.

    APC Layer: This layer adapts control variables (such as valve positions, fan speeds, and frequency adjustments) to real-time changes in raw material quality, fuel conditions, and operational parameters. It stabilizes the system and ensures smooth operation, relaying control commands to the DCS.

    DCS Layer: This layer collects data from the production line, feeding it to higher control layers while executing control commands from the APC.

    2.3.2 Mode Selection


    Mode selection is a macro instruction issued by the company from the perspective of economic objectives, directing the production department to ensure that production results meet economic targets.
    Currently, the modes are divided into three main categories:

    (1) High-output mode: This mode focuses on maximizing feed rates for the production process, including raw material mill feed, kiln feed, coal mill feed, and cement mill feed, while ensuring product quality. The goal is to produce as many qualified products as possible within the allowable production time.

    (2) Energy-saving mode: This mode aims to reduce energy consumption during production, thereby lowering the energy costs of the process. This includes reducing the electricity consumption of the raw material mill and cement mill, as well as the specific coal consumption and power consumption of the clinker.

    (3) Stability mode: This mode focuses on achieving stable operating conditions during production without excessive consideration of output or energy consumption. The aim is to stabilize kiln conditions or mill conditions.

    2.4 AI Modeling and Decision-Making


    AI modeling and decision-making are divided into two parts.


    (1) Using historical data for modeling, it establishes a relationship model between the input and output parameters of the production process. Taking the clinker burning system as an example, typical input parameters include: feed rate, the three ratios, outlet CO and O₂ from the first-stage cyclone, decomposition furnace outlet temperature, smoke chamber temperature, smoke chamber CO, smoke chamber NOx, secondary air temperature, and tertiary air temperature. Typical output parameters include specific coal consumption, power consumption, and free lime. Using a large amount of historical data, the relationship model between these input and output parameters is built.


    (2) Decision-making based on the model involves optimizing the target parameters.

    On the basis of this model, we obtain real-time production data and constraint data, such as feed rate, the three ratios, quality constraint indicators, and energy consumption constraint indicators. Through the model, the optimal target parameters, such as decomposition furnace outlet temperature, smoke chamber temperature, and secondary air temperature, are obtained. These serve as the control targets for the expert system.

    While optimizing, the system also predicts the corresponding energy consumption and product quality.

    2.5 Decision and Control Process Data Flow


    The process flow from decision-making to execution in an AI-based intelligent control system is as follows:


    (1) The real-time database collects various production data from the DCS, processes, and stores it.
    (2) The AI decision-making control system retrieves relevant data from the information system and real-time database, performs decision-making, and calculates the decision results.
    (3) The AI decision-making control system sends the decision values to the expert system, guiding the expert system’s operation.
    (4) The expert system downloads the set values to the DCS, which drives the actuators to achieve the control targets.

    3. AI Modeling and Decision-Making Process

    3.1 Modeling Using AI

    The AI system builds process models based on historical data, capturing relationships between key inputs (such as feed rates, oxygen levels, and kiln temperatures) and outputs (such as energy consumption and product quality). This model enables the system to optimize production in real-time.

    3.2 Decision-Making Optimization

    Based on real-time production data and constraints, the AI system calculates optimal control settings, such as decomposition furnace temperature and fan speeds. It also predicts the impact of these settings on energy consumption and product quality, providing more efficient and stable control.

    4.System Implementation

    This AI-driven system was applied to a 5000 t/d clinker production line, covering the raw material grinding, coal milling, clinker burning, cement grinding, and waste heat recovery systems. Results demonstrated improved stability in key parameters, with a 30% reduction in temperature fluctuations and a reduction in energy consumption per ton of clinker by 1 kg/t.

    5.Conclusion

    By incorporating AI decision-making into the traditional control and expert systems framework, this approach introduces economic considerations into the production process. The system optimizes control targets to reduce energy consumption, increase output, and improve overall operational efficiency. The successful application in a test plant shows that AI-based multi-mode production can effectively improve process control in the cement industry, paving the way for broader adoption of smart manufacturing technologies.

    Lesley Zhu

    Hi, this is Lesley Zhu, I have been working in the cement industry for more than 10 years, and I also have a professional team behind me. If you want to purchase cement spare parts or have related technical questions, please feel free to contact me.

    We'd like to work with you

    Send us a message if you have any questions or request a quote. Our team will give you a reply within 24 hours.