葛 亮,廖聪冲,肖启强,卓 勇,罗 明.燃气加臭广义预测精准控制算法研究[J].电子测量与仪器学报,2023,37(12):117-125
燃气加臭广义预测精准控制算法研究
Study on generalized predictive accurate control algorithm for gas odorization
  
DOI:
中文关键词:  燃气加臭控制  广义预测控制  系统辨识  加臭剂浓度  燃气安全
英文关键词:gas odorization control  generalized predictive control  system identification  odorant concentration  gas safety
基金项目:省科技计划项目(2023ZHCG0020)、西南石油大学自然科学“启航计划”项目(2023QHZ003)、南充市-西南石油大学市校科技战略合作项目(23XNSYSX0022 / 23XNSYSX0026)资助
作者单位
葛 亮 1. 西南石油大学 
廖聪冲 1. 西南石油大学 
肖启强 2. 中国石油西南油气田公司 
卓 勇 2. 中国石油西南油气田公司 
罗 明 1. 西南石油大学 
AuthorInstitution
Ge Liang 1. Southwest Petroleum University 
Liao Congchong 1. Southwest Petroleum University 
Xiao Qiqiang 2. Petro China Southwest Oil & Gasfield Company 
Zhuo Yong 2. Petro China Southwest Oil & Gasfield Company 
Luo Ming 1. Southwest Petroleum University 
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中文摘要:
      燃气加臭浓度精准控制对保证燃气的安全运输和使用具有重要作用。 但由于缺乏对终端用户处实际燃气加臭浓度的 监控,现有以燃气流量比例开环控制为代表的线性控制算法难以取得很好的控制效果。 为此,基于开环阶跃响应最小二乘法辨 识,以燃气终端用户处的加臭剂浓度为控制对象,建立了燃气加臭过程的 CARIMA 模型,在传统广义预测控制基础上提出了一 种改进型广义预测控制算法,并通过燃气加臭控制的仿真测试和现场实验验证了该控制算法的可行性。 研究结果表明,相较于 常规的燃气流量比例开环控制,改进型广义预测控制算法可实时在线精准控制终端用户处的加臭剂浓度,稳态误差小于 2 mg / m 3 ,工况变化时超调量小于 10%,平均绝对误差小于 1. 5 mg / m 3 ,可以满足燃气加臭过程精准控制需要。 研究成果在提高 燃气加臭效率和质量的同时,起到了节约成本和提升燃气安全的作用,具有良好的应用价值。
英文摘要:
      Accurate control of gas odorization concentration plays an important role in ensuring the safe transportation and use of gas. However, due to the lack of monitoring of the actual odorization effect at the end-users, the existing linear control algorithm represented by the gas flow rate proportional open-loop control is difficult to achieve a good control effect. For this reason, based on the least-squares identification of the open-loop step response and taking the odorant concentration at the gas end-users as the control object, a CARIMA model of the gas odorization process is established, and an improved generalized predictive control algorithm is proposed on the basis of the traditional generalized predictive control, and the feasibility of this control algorithm is verified by the simulation tests and field experiments of the gas odorization control. The research results show that compared with the conventional gas flow rate proportional openloop control, the improved generalized predictive control algorithm can accurately control the odorant concentration at the end-users in real-time online, with the steady-state error of less than 2 mg / m 3 , the overshoot of less than 10% when the working condition changes, and the average absolute error of less than 1. 5 mg / m 3 , which can meet the need of accurate control of gas odorization process. The research results improve the efficiency and quality of gas odorization while playing a role in saving costs and improving gas safety, which has good application value.
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