Trunkline Continuous Hopfield Neural Network-based Optimization Study

Gas transportation pipeline construction and management is in possession of high technology, high investment and primary risks. It is very important to get the preferred plan of gas transportation pipeline design and compressor station operation by means of optimization calculation. The optimization problem is complex and it is difficult to be solved with the general numerical optimize algorithm. The research about intelligent optimize algorithm is developed sophisticatedly and it can effectively solve the gas transportation pipeline optimization problem.In this paper, the method of annually costs is used to found the collective optimization model including gas transportation pipeline optimum design and compressor station optimum operation. The two models respectively belong to fix discrete optimization and combination optimization, and as a result, Continuous Hopfield Neural Network (CHNN) optimize algorithm is first introduced into solving gas transportation pipeline collective optimization system in this paper. In pipeline optimization design CHNN model, Lagrange Function is used to found the energy function and equation of motion about optimize parameters. In compressor station optimization operation CHNN model, Penalty Function and quadratic function structuring method are used to found the energy function and equation of motion about optimize parameters. In order to solve robustness instability and it easily falls into the locally optimal solution about CHNN, the Simulated Annealing algorithm is introduced and the CHNN—SA hybrid algorithm is designed to solve the gas transportation pipeline collective optimization model.At last, the paper write the calculated program with Visual Basic 6.0 and Borland C++ Builder 6.0, and two examples of calculation examine the paper's research result. The result present that it can successfully and effectively solve gas transportation pipeline collective optimization model with the hybrid optimize algorithm composed of CHNN algorithm and Simulated Annealing algorithm.