Process control and optimization method based on pneumatic conveying system

Abstract

The invention provides a process control and optimization method based on a pneumatic conveying system, and aims to improve the efficiency and reliability of the pneumatic conveying system through an integrated sensor network, real-time data processing and an intelligent control algorithm. The method comprises four core modules, namely a data source integration module, a data acquisition and processing module, a model + algorithm module and a closed-loop control module. The data source integration module collects data from the DCS / PLC system and the sensor network; the data acquisition and processing module is responsible for data cleaning, format conversion and abnormal value detection; the model + algorithm module is combined with material prediction, step sequence planning, material-gas ratio calculation and a conveying control strategy model to realize deep learning and optimal control; the closed-loop control module adjusts the conveying process according to real-time feedback, and operation stability is ensured. The system is suitable for industries of chemical engineering, building materials, environmental protection, food and the like, and can effectively reduce energy consumption, reduce pipeline abrasion, improve system stability and reduce maintenance cost.

Description

Technical Field

[0001] The present invention relates to the field of industrial automation, and in particular to a process control and optimization method based on a pneumatic conveying system. The method aims to improve the efficiency and reliability of the pneumatic conveying system through advanced control technology, and is widely used in material handling and transportation processes in industries such as chemical, building materials, environmental protection and food. Background Art

[0002] Pneumatic conveying system is a technology that uses airflow to transport powder and granular materials from one location to another. It is widely used in industries such as chemical, building materials, environmental protection and food. In these industries, pneumatic conveying system is favored because of its high efficiency, closed and continuous conveying method. However, the existing pneumatic conveying system still faces many challenges in practical application.

[0003] First of all, pipeline blockage is one of the most common problems in pneumatic conveying systems, usually caused by excessive particle size, high humidity or unreasonable pipeline design. Pipeline blockage not only leads to production stagnation, but also may cause equipment damage and safety hazards.

[0004] Secondly, the wear problem is also a major pain point of the pneumatic conveying system. The friction between high-speed airflow and solid particles will cause serious wear of pipes and valves, increase maintenance frequency and replacement costs, and affect the long-term stable operation of the system.

[0005] In addition, the high energy consumption of pneumatic conveying systems is also an urgent problem to be solved. Especially when the conveying distance is long or the material density is high, the energy consumption of the system increases significantly, which brings economic burden to the enterprise.

[0006] Operational complexity is also a major challenge for pneumatic conveying systems. The operation of the system requires professional knowledge and experience. When faced with complex control parameters, operators are prone to misoperation, which affects the normal operation of the system.

[0007] Finally, pneumatic conveying systems have high maintenance costs, mainly due to wear and tear and the cost of component replacement. In addition, the stability of the system is affected by many factors, including material flow characteristics, equipment performance and environmental conditions, which may cause the system to become unstable during operation.

[0008] In order to solve these problems, a new process control and optimization method is urgently needed to improve the overall performance of the pneumatic conveying system, reduce energy consumption and maintenance costs, and ensure the stability and reliability of the system. Through the precise control of material characteristics, conveying parameters and system status, the shortcomings of the existing pneumatic conveying system can be effectively improved. Summary of the invention

[0009] The core of this invention is a pneumatic conveying intelligent auxiliary system, which aims to comprehensively improve the intelligence level of the pneumatic conveying process through a highly integrated sensor network, advanced data processing technology, precise model algorithm and closed-loop control system. The system can not only monitor and optimize the conveying process in real time, but also predict and solve potential problems, thereby improving the efficiency of the system, reducing energy consumption, reducing maintenance costs, and enhancing the stability of the system.

[0010] 3.1 Data Source

[0011] The data source integration module of the present invention is the basis of the intelligent auxiliary system, which includes data collection from the DCS (distributed control system) or PLC (programmable logic controller) on the factory side, and real-time data collected by the integrated sensor network. The optimization of this module aims to achieve comprehensive and accurate data collection of the pneumatic conveying system, providing a solid foundation for subsequent data processing and intelligent control.

[0012] 3.1.1 Factory-side data collection

[0013] The system directly collects key operating data from the factory's existing DCS or PLC system, including but not limited to the operating data of the upstream pneumatic conveying system and the operating status of the air source equipment, the working mode of the feeding device, the pressure and flow of the conveying pipeline, etc. This integration method reduces the need for additional hardware and reduces the cost and complexity of system transformation.

[0014] 3.1.2 Sensor Integration

[0015] In addition to the data from the factory side, the system also collects real-time data by deploying a high-precision sensor network, such as level meters, thermal mass flow meters, pressure & temperature sensors, etc. These sensors are strategically deployed at key locations in the delivery pipeline to ensure that key parameter changes that may cause delivery problems can be captured.

[0016] 3.1.3 Data Synchronization and Fusion

[0017] The system can synchronize data collected from DCS / PLC and sensor networks, and integrate these data to provide a more comprehensive view of system status through advanced data fusion technology. This data fusion technology can improve the accuracy and reliability of data and provide higher quality input for intelligent control algorithms.

[0018] 3.1.4 Data Preprocessing

[0019] Before entering the intelligent control algorithm, the collected data will go through a series of preprocessing steps, including filtering, denoising, normalization, etc., to ensure the quality of the data and reduce errors in subsequent processing.

[0020] 3.2 Data collection and processing

[0021] The data acquisition and processing module of the present invention is a key component of the pneumatic conveying intelligent auxiliary system. It is responsible for processing real-time data collected from the DCS / PLC system and the sensor network (including the material level meter and the thermal mass flow meter). The module adopts an advanced real-time data calculation scheme to ensure efficient processing and permanent storage of data for subsequent analysis and decision support.

[0022] 3.2.1 Time Series Database

[0023] The system uses a time series database to store and manage time series data collected from the pneumatic conveying system. The time series database is specially optimized for processing data points recorded in chronological order, such as sensor readings and equipment status. It can efficiently handle large amounts of data writing and fast query, meeting the needs of real-time monitoring and historical data analysis.

[0024] 3.2.2 Data processing module

[0025] The data processing module is responsible for real-time processing of raw data, including data cleaning, format conversion, outlier detection, and data aggregation. This module can identify and correct anomalies or errors in the data, ensuring that only accurate and reliable data is used for further analysis and control decisions. In addition, the data processing module is also responsible for normalizing and standardizing the data to meet the input requirements of the intelligent control algorithm and improve the accuracy and response speed of the algorithm.

[0026] 3.2.3 Permanent storage solution

[0027] The permanent storage solution of the present invention refers to further saving the data processed by the time series database into an Oracle database. Oracle database is well-known for its high reliability, high performance and powerful data management functions, and is very suitable as a permanent data storage solution for enterprise-level applications. By migrating data from the time series database to the Oracle database, the system can use Oracle's advanced data management and analysis tools, such as data warehouse functions, data mining and complex query processing, to provide enterprises with in-depth data insights and long-term data retention strategies. The permanent storage solution also includes a data backup and recovery mechanism to prevent data loss or damage, ensuring the business continuity and data integrity of the system.

[0028] 3.3 Model + Algorithm

[0029] The "model + algorithm" module of the present invention is the core of the pneumatic conveying intelligent auxiliary system. It combines advanced data models and algorithms to achieve deep learning and optimized control of the pneumatic conveying system. The following is a detailed description of the module:

[0030] 3.3.1 Material Forecasting Model

[0031] The material prediction model is an important part of the present invention, which is responsible for predicting the amount of material that will be produced in the future. This model is crucial for optimizing the scheduling and operation of the pneumatic conveying system.

[0032] 3.3.1.1 Prediction Mechanism:

[0033] The model uses historical transportation data, material production data, and factory operation data, combined with advanced machine learning algorithms such as time series regression models and neural networks, to predict future material generation. The model takes into account a variety of factors that affect material generation, including production plans, equipment operating status, maintenance cycles, and changes in market demand.

[0034] 3.3.1.2 Data Input

[0035] The model input includes upstream data, such as raw material test data, material gas ratio measured data and historical transportation records; the system also considers the data provided by the DCS or PLC system on the factory side.

[0036] 3.3.1.3 Prediction Output

[0037] The model output predicts the amount of material that will be produced in the next 24 hours, providing decision support for the scheduling and control of the pneumatic conveying system.

[0038] 3.3.2 Step Planning Model

[0039] The step planning model is another key component of the present invention, which focuses on optimizing the conveying sequence of multiple feeding devices in the pneumatic conveying system. The model uses the principles of operations research and advanced solver technology to calculate the optimal sequence of conveying units to improve the overall conveying efficiency and reduce energy consumption.

[0040] 3.3.2.1 Model Objectives

[0041] The main goal of the step planning model is to determine the optimal conveying sequence so that all feeding devices can operate in the most efficient manner while meeting production requirements and equipment constraints.

[0042] 3.3.2.2 Solver Technology

[0043] The model uses solver techniques such as Linear Programming (LP), Integer Programming (IP), Mixed Integer Linear Programming (MILP), etc.

[0044] (MILP) or genetic algorithm (GA) to solve the optimal transport sequence. The solver is able to handle complex constraints and find the best or near-optimal solution under the given constraints.

[0045] 3.3.2.3 Model Input

[0046] The input is the material level of the temporary hopper, the hard constraints of pneumatic conveying, the material quantity prediction data, and the material-gas ratio sample library.

[0047] 3.3.2.4 Model Output

[0048] The output is the optimal conveying sequence plan, which specifies the conveying time and sequence of each feeding device. The plan can be adjusted dynamically to cope with production changes or sudden changes in equipment status.

[0049] 3.3.2.5 Optimization effect

[0050] Through the step planning model, the reasonable amount of materials in the temporary storage device or pneumatic conveying pump can be accurately grasped, and the conveying frequency can be reasonably arranged, which can not only reduce energy consumption, but also reduce the heavy use of equipment, thereby reducing maintenance costs.

[0051] 3.3.3 Material-gas ratio calculation model

[0052] The material-gas ratio calculation model is a key component of the present invention, focusing on reducing the energy consumption and pipeline wear of the pneumatic conveying system by accurately calculating and optimizing the material-gas ratio (material-gas ratio). The model evaluates the working conditions and analyzes the material-gas ratio in combination with measured data to optimize the conveying efficiency and reduce pipeline wear.

[0053] 3.3.3.1 Model Objectives

[0054] The main goal of the model is to reduce energy consumption and the flow rate of the ash-gas mixture in the pipeline by accurately controlling the material-gas ratio, thereby reducing pipeline wear and reducing the risk of pipeline blockage.

[0055] 3.3.3.2 Model Input

[0056] The model is based on measured data to analyze the impact of material-gas ratio on conveying efficiency and energy consumption. By collecting and analyzing the physical properties of the material (such as particle size, density, hygroscopicity) and parameters during the conveying process (such as pressure and flow), the model can determine the optimal material-gas ratio.

[0057] 3.3.3.3 Model output

[0058] The model output is the optimal material-gas ratio and corresponding conveying parameters, providing decision support for real-time monitoring and control of the pneumatic conveying system.

[0059] 3.3.3.4 Model Effect

[0060] Reduce energy consumption and wear: By optimizing the gas ratio, energy consumption can be significantly reduced, production costs can be reduced, and transportation efficiency can be guaranteed. A reasonable material-gas ratio can improve the stability of system operation, reduce the deposition of materials in the pipeline, and reduce the risk of blockage. A lower gas-solid ratio, that is, a relatively small amount of gas, is usually used for the transportation of large amounts of materials in dense phase conveying. The correct gas-solid ratio ensures the continuous transportation of materials and the stability of the system, and can effectively avoid problems such as blockage of the conveying pipeline and excessive pressure.

[0061] 3.3.4 Transportation control strategy model

[0062] The conveying control strategy model is a key component of the present invention, focusing on executing the strategies produced by other models and dynamically adjusting the operation of the pneumatic conveying system based on real-time data and empirical strategies. The model mainly uses PID (proportional-integral-differential) technology to achieve precise conveying control.

[0063] 3.3.4.1 Model Objectives

[0064] The goal of the conveying control strategy model is to ensure that the pneumatic conveying system can operate stably and efficiently under various working conditions, while reducing energy consumption and pipeline wear and reducing the risk of pipeline blockage.

[0065] 3.3.4.2 Model Input

[0066] The input of the model includes the original conveying strategy (based on experience), material-gas ratio, material volume, gas volume,

[0067] Key parameters such as temporary hopper material level, air inlet pipe pressure, regulating valve control logic, etc.

[0068] 3.3.4.3 Model output

[0069] The model outputs are adjusted conveying parameters and control signals, which are directly used for real-time control of the pneumatic conveying system.

[0070] The control signal includes specific operating instructions for the gas source equipment, feeding device and regulating valve to achieve the best conveying effect.

[0071] 3.3.4.4 Process Control

[0072] Based on real-time data and preset control targets, PID control technology is used to adjust the three parameters of proportion (P), integral (I) and differential (D) to give the optimal control signal and adjust the operating status of the feed valve, air source equipment and regulating valve.

[0073] 3.3.4.5 Model Effect

[0074] By precisely controlling the material-gas ratio and conveying parameters, the model helps reduce energy consumption and the velocity of the ash-gas mixture in the pipeline, thereby reducing pipeline wear. Accurate loading and reasonable pressure (or flow) control help reduce the risk of pipeline blockage and improve conveying efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a sensor integration topology diagram in the data source of the present invention.

[0076] Figure 2 It is a schematic diagram of the technical ideas in the model + algorithm of the present invention.

[0077] Implementation

[0078] Closed-loop control is a key implementation in the pneumatic conveying intelligent auxiliary system, which ensures that the system can adjust itself according to real-time data feedback to achieve optimal conveying efficiency and operational stability. The following is a detailed description of the closed-loop control implementation:

[0079] 5.1 Implementation Plan Deployment

[0080] After deploying the intelligent auxiliary system in the pneumatic conveying system, the system will monitor and analyze key parameters in the conveying process in real time, such as material quantity, gas volume, material-gas ratio, temporary hopper level, inlet pipeline pressure, etc. The system will generate control strategies based on these real-time data, which will guide operators to perform specific operations.

[0081] 5.2 Operation and feedback from operators

[0082] The operator performs operations according to the control strategy generated by the system, such as adjusting the gas source equipment, feeding device and regulating valve, etc. After the operation, the operator will evaluate the operation results, provide feedback to verify the effectiveness of the control strategy, and make manual adjustments as needed.

[0083] 5.3 Automatic Control and DCS / PLC Integration

[0084] After preliminary manual operation and evaluation, the effectiveness of the control strategy is confirmed, and the system will automatically connect these strategies to the DCS or PLC system for trial operation. This step is the key transition of the system from auxiliary decision-making to actual control, ensuring the practical application of the control strategy and the automated operation of the system.

[0085] 5.4 Continuous Monitoring and Optimization

[0086] The system will continuously monitor the conveying process and make self-adjustments based on real-time data feedback to optimize conveying efficiency and stability. This self-adjustment capability enables the system to quickly respond to changes in the external environment and fluctuations in internal parameters to maintain the optimal state of the conveying process.

[0087] 5.5 Improved system stability and reliability

[0088] The closed-loop control implementation scheme improves the stability and reliability of the system, enables the pneumatic conveying system to adapt to production changes more flexibly, and improves the ability to respond to emergencies.

[0089] 5.6 Long-term benefits

[0090] In long-term operation, closed-loop control can reduce production delays caused by improper delivery planning through continuous monitoring and adjustment, improve customer satisfaction, and reduce maintenance costs.

[0091] Through the closed-loop control implementation plan, the pneumatic conveying intelligent auxiliary system can achieve a complete closed loop from monitoring to control, greatly improving the intelligence level of the system and ensuring the efficiency, stability and safety of the conveying process. This implementation plan not only improves the accuracy of the operation, but also enhances the system's adaptability and overall performance.

Claims

1. A process control and optimization method based on a pneumatic conveying system, characterized in that: The following steps are involved: a. Data source integration: collecting data from the DCS or PLC system on the factory side and integrating real-time data collected by the sensor network; b. Data collection and processing: using a time series database to store and manage time series data, and performing data cleaning, format conversion, outlier detection, and data aggregation; c. Model + algorithm, combining data model and algorithm to achieve deep learning and optimal control of pneumatic conveying system; d. Closed-loop control, self-adjusting based on real-time data feedback to achieve optimal conveying efficiency and operating stability.

2. The method according to claim 1, characterized in that Data source integration includes factory-side data collection, sensor integration, data synchronization and fusion, and data preprocessing.

3. The method according to claim 1 or 2, characterized in that: Data collection and processing includes time series database, data processing module and permanent storage solution.

4. The method according to claim 1 or 2, characterized in that: The model + algorithm includes material prediction model, step planning model, material-gas ratio calculation model, and transportation control strategy model.

5. The method according to claim 4, characterized in that The conveying control strategy model uses PID technology to dynamically adjust the operation of the pneumatic conveying system according to parameters such as the original conveying strategy, gas ratio, material quantity, gas volume, temporary accumulator level, air inlet pipeline pressure, and regulating valve control logic.

6. The method according to claim 1, characterized in that Closed-loop control includes implementation plan deployment, operator operation and feedback, automatic control and DCS / PLC integration, continuous monitoring and optimization, and system stability and reliability improvement.

7. An intelligent auxiliary system for pneumatic conveying, characterized in that: A computer program for implementing the method according to any one of claims 1 to 6 is included.

8. The system according to claim 7, characterized in that The computer program further includes modules for data source integration, data acquisition processing, model+algorithm, and closed-loop control.

9. A computer-readable storage medium, characterized in that: A computer program for implementing the method according to any one of claims 1 to 6 is stored.

10. The storage medium according to claim 9, characterized in that The computer program further includes modules for data source integration, data acquisition processing, model+algorithm, and closed-loop control.

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