Design of a neural network based predictive controller for natural gas pipelines in transient state

Abstract In a natural gas network the gas pressure decreases continuously due to friction. The compressor stations are placed in strategic locations of networks to make up for the lost pressure. This ensures the delivery of time-varying customer demands in desirable pressure. Howbeit, the compressor's operational costs constitute the major portion of a network costs. Different approaches can be used to find the optimum operation of the gas network compressor's when the demand profiles are known from the historical data. However, the demand profiles may become different from their long time average. For this case, a novel on-line predictive control scheme is presented in this work. This scheme finds the near optimum operation of the compressor more easily and in much less computational time, while eliminating the necessity of re-solving the optimization problem. The proposed strategy utilizes two multi layered neural networks (MLNNs) for on-line prediction and control tasks. The NN predictor is used for on-line prediction of highly nonlinear dynamics of the gas network in transient state. The on-line NN controller uses the prediction information to find the near optimum control inputs (rotational speeds of the compressors) to provide the new desired demands. This can be achieved by tracking the previously obtained optimum outlet pressures of the network. The controller results are validated with another controller which uses the particle swarm optimization (PSO) algorithm as the optimizer. To investigate the controller operation in performing the optimization task, its simulation results are compared with global optimum results, which assert its suitable performance. To investigate the robustness of the control scheme, the network outlet demand flow rates are changed in two different scenarios. Moreover, the performance of the controller is evaluated in the presence of noise and disturbances, which confirms its efficiency and accuracy.

[1]  O. Agamennoni,et al.  A nonlinear model predictive control system based on Wiener piecewise linear models , 2003 .

[2]  K. Leblebicioglu,et al.  Optimal Control of Gas Pipelines via Infinite-Dimensional Analysis , 1996 .

[3]  Reza Madoliat,et al.  Dynamic simulation of gas pipeline networks with electrical analogy , 2017 .

[4]  Adam P. Piotrowski,et al.  A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling , 2013 .

[5]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .

[6]  E. Shashi Menon,et al.  Gas pipeline hydraulics , 2005 .

[7]  Biao Huang,et al.  A data driven subspace approach to predictive controller design , 2001 .

[8]  Victor M. Zavala,et al.  Large-scale optimal control of interconnected natural gas and electrical transmission systems , 2016 .

[9]  Michael Chertkov,et al.  Control policies for operational coordination of electric power and natural gas transmission systems , 2016, 2016 American Control Conference (ACC).

[10]  Maciej Lawrynczuk,et al.  On improving accuracy of computationally efficient nonlinear predictive control based on neural models , 2011 .

[11]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[12]  R. Whalley,et al.  Gas pipeline modelling and control , 2015 .

[13]  Tatsuhiko Kiuchi,et al.  An implicit method for transient gas flows in pipe networks , 1994 .

[14]  Hans Aalto.  Model Predictive Control of Natural Gas Pipeline Systems - a case for Constrained System Identification , 2015 .

[15]  Lawrence Megan,et al.  Dynamic modeling and linear model predictive control of gas pipeline networks , 2001 .

[16]  E. Andrew Boyd,et al.  Efficient operation of natural gas transmission systems: A network-based heuristic for cyclic structures , 2006, Comput. Oper. Res..

[17]  Ajith Abraham,et al.  Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[18]  Glenn R. Price,et al.  Evaluating the Effective Friction Factor and Overall Heat Transfer Coefficient During Unsteady Pipeline Operation , 1999 .

[19]  Mohsen Hadian,et al.  Using artificial neural network predictive controller optimized with Cuckoo Algorithm for pressure tracking in gas distribution network , 2015 .

[20]  Satish Udpa,et al.  Advanced signal processing of magnetic flux leakage data obtained from seamless gas pipeline , 2002 .

[21]  J. Osiadacz Andrzej.  Hierarchical control of transient flow in natural gas pipeline systems , 1998 .

[22]  Mohd Amin Abd Majid,et al.  Simulation model for natural gas transmission pipeline network system , 2011, Simul. Model. Pract. Theory.

[23]  Suming Wu,et al.  Model relaxations for the fuel cost minimization of steady-state gas pipeline networks , 2000 .

[24]  M. Kemal Leblebicioğlu,et al.  State estimation of transient flow in gas pipelines by a Kalman filter-based estimator , 2016 .

[25]  M. Neuroth,et al.  Improved modelling and control of oil and gas transport facility operations using artificial intelligence , 2000, Knowl. Based Syst..

[26]  Arthur J. Helmicki,et al.  Vibration-based cable condition assessment: A novel application of neural networks , 2018, Engineering Structures.

[27]  Arthur J. Helmicki,et al.  Stay Cable Tension Estimation of Cable-Stayed Bridges Using Genetic Algorithm and Particle Swarm Optimization , 2017 .

[28]  Ajith Abraham,et al.  Inertia Weight strategies in Particle Swarm Optimization , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[29]  Hesam Ahmadian Behrooz,et al.  Modeling and state estimation for gas transmission networks , 2015 .

[30]  A. Osiadacz,et al.  Simulation of non-isothermal transient gas flow in a pipeline , 2001 .

[31]  Victor M. Zavala,et al.  Stochastic optimal control model for natural gas networks , 2014, Comput. Chem. Eng..

[32]  Yie-Chien Chen,et al.  A model reference control structure using a fuzzy neural network , 1995 .

[33]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[34]  Esmaeel Khanmirza,et al.  Transient Optimization of Natural Gas Networks Using Intelligent Algorithms , 2018, Journal of Energy Resources Technology.

[35]  Lorenz T. Biegler,et al.  Economic Nonlinear Model Predictive Control for periodic optimal operation of gas pipeline networks , 2013, Comput. Chem. Eng..

[36]  Tarek Zayed,et al.  Artificial neural network models for predicting condition of offshore oil and gas pipelines , 2014 .

[37]  Rezvan Alamian,et al.  A state space model for transient flow simulation in natural gas pipelines , 2012 .

[38]  K. S. Chapman,et al.  Nonisothermal Transient Flow in Natural Gas Pipeline , 2008 .

[39]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[40]  Chi-Huang Lu,et al.  Wavelet Fuzzy Neural Networks for Identification and Predictive Control of Dynamic Systems , 2011, IEEE Transactions on Industrial Electronics.

[41]  B. De Schutter,et al.  Distributed Predictive Control for Energy Hub Coordination in Coupled Electricity and Gas Networks , 2010 .

[42]  M. Morari,et al.  Model Predictive Control of Gas Pipeline Networks , 1986, 1986 American Control Conference.

[43]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[44]  Adam P. Piotrowski,et al.  Estimation of parameters of the transient storage model by means of multi-layer perceptron neural networks / Estimation des paramètres du modèle de transport TSM au moyen de réseaux de neurones perceptrons multi-couches , 2008 .

[45]  Michael Chertkov,et al.  Optimal control of transient flow in natural gas networks , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[46]  Esmaeel Khanmirza,et al.  Application of PSO and cultural algorithms for transient analysis of natural gas pipeline , 2017 .

[47]  A. Talasaz,et al.  Adaptive Predictive Control Application in Gas Distribution Network: A Case Study , 2007, 2007 Information, Decision and Control.

[48]  Ksenia O. Kosova,et al.  Mathematical and computer models for identification and optimal control of large-scale gas supply systems , 2019 .