面向抽油机节能的GRNN过程建模及工艺参数优化

Beam pumping process modeling and parameters optimization based on generalized regression neural networks for energy conservation

针对抽油机系统效率低,能耗大的问题,提出一种基于数据挖掘的抽油机建模及节能优化方法。抽油机的工艺参数理想与否是决定抽油机效率的一个重要因素,而抽油机模型的有效性又是优化工艺参数的关键。抽油机工作过程是一个复杂非线性系统,很难用准确的数学模型描述,广义回归神经网络(generalized regression neural network,GRNN)非线性映射能力强、容错性高,适于解决非线性系统建模问题。为此,提出利用GRNN确定工艺参数与增产节能指标的映射关系,建立抽油机模型;实验结果表明模型的拟合度较好,建模效果良好。紧接着,运用具有智能特性的Pareto向量评价微粒群算法(vector evaluated particle swarm optimization based on pareto,VEPSO-BP)对模型进行搜索寻优,确定工艺参数的最优值,并用优化后的工艺参数指导实际生产;实验结果表明优化后的抽油机采油系统产量提高6.6%以上,用电量降低4.1%以上,验证了所提方法的可行性和有效性。

This paper presents a data-mining-based beam pumping unit process modeling and parameters optimization method to solve the problem of inefficiency and energy-intensive of beam pumping unit.The ideality of process parameters is one of the main factors influencing system efficiency and energy consumption,while the effectiveness of mode plays a key role in process parameters choosing.Beam pumping unit system is a complicated nonlinear system,and is hardly to be precisely described by precise mathematical models.Generalized regression neural network（GRNN）,which is powerful in nonlinear mapping and generalization,is suitable for nonlinear systems.Therefore,GRNN is proposed to model the beam pumping unit in this paper,and the experimental results show that the fitness is good.Then the trained model is applied to optimize the decision parameters by vector evaluated particle swarm optimization based on Pareto（VEPSO-BP）,and at last the resulting parameters are applied to the production.Experimental results show that after using the optimal parameters,the efficiencies and energy consumptions increase more than 6.6% and decrease more than 4.1% respectively,which illustrates the feasibility and effectiveness of the proposed method.