Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units
Source abstract
Abstract
Cementing is a critical link in oil and gas exploitation, in which slurry density control is particularly important. In this study, we examined a slurry mixing control system in order to solve the problem of time delays in the mixing system. The model of a slurry mixing system was built in accordance with the system’s structure. A Smith fuzzy PID (proportion integration differentiation) composite control solution is proposed herein, and the simulation results show that the adjustment time and overshoot are lower than those of the conventional PID control and Smith predictive compensation control. A genetic algorithm is utilized to optimize the quantization factor and scale factor of the Smith fuzzy PID controller. Following optimization, the rise time of the controller was found to be 0.45 s, which represents a decrease of 35.9%, the overshoot was reduced by 0.4%, and the stabilization time was reduced by 36.6%. Afterward, we built a cementing slurry mixing simulation experimental platform, and experiments were used to verify the feasibility and superiority of the Smith fuzzy PID controller optimized by the genetic algorithm in comparison with the conventional controllers. The study results thus provide a scientific basis for the engineering application of the autonomous control technology of the slurry mixing system in cementing units.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Belt Conveyor Systems Engineering · Mineral Processing and Grinding · Industrial Engineering and Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Wanhua Chemical Group Co., Ltd., Yantai 264006, China
College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, China
National Engineering Research Center of Marine Geophysical Prospecting and Exploration and Development Equipment, Qingdao 266580, China
Cosco Shipping Heavy Industry Co., Ltd., Shanghai 200135, China
References (25)
Show source-provided references
1. Research on the New Problems and Development Direction Faced by Domestic Cementing at Present
2. Stable LPV Realization of the Smith Predictor10.1080/00207721.2014.998319 ↗
3. Bai, J.B. (2006). Research on Adaptive Control of Test Room Air Conditioning System. [Master’s Thesis, Southeast University].
4. Application of PSO Tuned Fuzzy Logic Controller for LFC of Two-area Power System with Redox Flow Battery and PV Solar Park10.1016/j.asej.2022.101710 ↗
5. Main Steam Pressure Control Technology Based on Mismatch Compensated Smith-RBF Neural Network
6. Adaptive Feedforward and Feedback Compensation Method for Real-time Hybrid Simulation Based on a Discrete Physical Testing System Model10.1080/13632469.2020.1823912 ↗
7. Wang, J.S. (2023). Research on Precise Train Stopping Algorithm Based on Particle Swarm PID-Smith Controller. Sci. Tech. Inno., 15–19.
8. An Improved Genetic Algorithm Optimization Fuzzy Controller Applied to the Wellhead Back Pressure Control System10.1016/j.ymssp.2020.106708 ↗
9. Using the Smith PSO-Fuzzy PID Model to Control Electrical Conductivity of Nutrient Solution of Precision Fertilization
10. A neuro-fuzzy model for online optimal tuning of pid controllers in industrial system applications to the mining sector10.1109/TFUZZ.2019.2923963 ↗
11. Online tuning of pid controller using a multilayer fuzzy neural network design for quadcopter attitude tracking control10.3389/fnbot.2020.619350 ↗
12. Design of self-organizing intelligent controller using fuzzy neural network10.1109/TFUZZ.2017.2785812 ↗
13. Fuzzy controllers with neural network predictor for second-order linear systems with time delay10.1109/ACCESS.2020.3036161 ↗
14. Fang, Y.K., Cheng, C.Y., Dong, Z., Min, H.G., and Zhao, X.M. (2020, January 27–28). A fault diagnosis framework for autonomous vehicles based on hybrid data analysis methods combined with fuzzy PID control. Proceedings of the International Conference on Unmanned Systems, Harbin, China.10.1109/ICUS50048.2020.9274856 ↗
15. Lei, Y., Wu, Y.Q., Yuan, Y.Q., and Chen, C. (2019, January 12–13). Intelligent neural network controller optimization and simulation using GA. Proceedings of the International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Changsha, China.10.1109/ICITBS.2019.00131 ↗
16. A fuzzy pid controller with nonlinear compensation term for mold level of continuous casting process10.1016/j.ins.2020.06.024 ↗
17. Fuzzy sampled-data control for synchronization of t-s fuzzy reaction-diffusion neural networks with additive time-varying delays10.1109/TCYB.2020.2996619 ↗
18. Adaptive neuro-fuzzy pid controller based on twin delayed deep deterministic policy gradient algorithm10.1016/j.neucom.2020.03.063 ↗
19. Fathollahi, A., and Andresen, B. (2023). Multi-Machine Power System Transient Stability Enhancement Utilizing a Fractional Order-Based Nonlinear Stabilizer. Fractal Fract., 7.10.3390/fractalfract7110808 ↗
20. Robust Artificial intelligence Controller for Stabilization of Full-Bridge Converters Feeding Constant Power Loads
21. Mpalaskas, A.C., Matikas, T.E., and Aggelis, D.G. (2015). Acoustic Emission and Related Non-Destructive Evaluation Techniques in the Fracture Mechanics of Concrete, Woodhead Publishing.
22. Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques10.1016/j.conbuildmat.2022.126500 ↗
23. Liu, Y.H. (2022). Research on Speed Tracking Control of Permanent Magnet Maglev Train Based on Predictive Fuzzy PID Control Algorithm. [Master’s Thesis, Jiangxi University of Science and Technology].
24. Deep Reinforcement Learning-PI Air Rudder Servo System Control Strategy Based on Genetic Algorithm Optimization
25. Simulation of Stellar Spectrum Controlled by Fuzzy PID10.37188/OPE.20233111.1619 ↗