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作 者:张磊 于建林 周明军 万敏[2] 廖茁栋 ZHANG Lei;YU Jianlin;ZHOU Mingjun;WAN Min;LIAO Zhuodong(CPECC Southwest Company,Chengdu,Sichuan,610041,China;School of Mechanical Engineering,Southwest Petroleum University,Chengdu,Sichuan,610500,China)
机构地区:[1]中国石油工程建设有限公司西南分公司,四川成都610041 [2]西南石油大学机电工程学院,四川成都610500
出 处:《天然气与石油》2023年第1期141-146,共6页Natural Gas and Oil
基 金:中国石油天然气集团有限公司科学研究与技术开发项目“煤炭地下气化关键技术研究与先导试验课题五——地面集输处理分离工艺研究与应用——注采集联动控制原理研究”(KY2019-08-03)。
摘 要:煤炭地下气化(Underground Coal Gasification, UCG)实现了地下固体煤炭转为气体能源的封闭开发,可形成天然气能源的战略接替,是保障国家能源安全的重要策略,也是实现国家新时代能源高质量发展的有力手段。由于煤炭地下气化反应过程具有时变性和大滞后的特点,且无法得到精确的数学模型,给煤炭地下气化控制带来较大挑战。为改善煤炭地下气化的控制效果,在研究煤炭地下气化工艺的基础上,根据煤炭地下气化过程工程数据对煤炭地下气化过程进行了数学建模,设计了针对煤炭地下气化控制的内模控制算法。仿真结果表明,设计的控制算法与传统的PID控制以及Smith预估控制相比较,内模控制算法在模型失配时控制效果更好,更适合于对模型不确定的煤炭地下气化过程的有效控制。研究结果可为煤炭地下气化实际生产过程的相关研究提供参考。Underground coal gasification achieves the development of underground solid coal into gas energy in an enclosed space. It can form a strategic replacement of natural gas energy, which is not only an important strategy to ensure national energy security, but also a powerful means to realize the high-quality development of national energy supply in the new era. But the reaction process of underground coal gasification has large variation with time and large time lag in terms of feedback of process parameters, and there is no accurate mathematical model to simulate the process. This brings great challenges to the control of underground coal gasification. To improve the control effectiveness of underground coal gasification, this paper studied the underground coal gasification process and process control strategy, established the mathematical model of the process of underground coal gasification according to the engineering data provided by the underground coal gasification experiment, and designed the internal model control algorithm for the control of underground coal gasification. The simulation results show that the designed algorithm has better control effect than traditional PID control and Smith predictive controller in the case of model mismatch, and is more suitable for the control of underground coal gasification with model uncertainty. The research results can be used as a reference for the relevant research on the actual production process of underground coal gasification.
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