Ensembles‐based and GA‐based optimization for landfill gas production

The efficient and economic production of landfill gases (LFG) by optimally adjusting LFG production settings is of high interest as a promising source of biomass energy. A key obstacle in LFG production optimization is the large-scale and complex system with overwhelming uncertainty and heterogeneity. We propose a simplified ensemble-based optimization (EnOpt) method to solve the LFG production optimization problem when constraints are not a concern, where the gradient information is obtained from an ensemble of realizations of the system. For constrained optimization, a novel parameterless genetic algorithm is proposed and successfully applied to the simulated LFG process. The effectiveness of the proposed (EnOpt) method and the parameterless genetic algorithm is demonstrated with the simulation of a landfill and gas generation and transport therein, using a parallel computation strategy. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2063–2071, 2014

[1]  Muhammad Sahimi,et al.  Computer simulation of gas generation and transport in landfills: VI—Dynamic updating of the model using the ensemble Kalman filter , 2012 .

[2]  Scott Kirkpatrick,et al.  Optimization by simulated annealing: Quantitative studies , 1984 .

[3]  Muhammad Sahimi,et al.  Computer simulation of gas generation and transport in landfills—I: quasi-steady-state condition , 2002 .

[4]  Dongxiao Zhang,et al.  New method for reservoir characterization and optimization using CRM–EnOpt approach , 2011 .

[5]  Ho-Jeen Su Smart Well Production Optimization Using an Ensemble-Based Method , 2009 .

[6]  Slobodan P. Simonovic,et al.  Optimal Operation of Reservoir Systems using Simulated Annealing , 2002 .

[7]  Muhammad Sahimi,et al.  Computer simulation of gas generation and transport in landfills. III: Development of lanfills' optimal model , 2007 .

[8]  L. Durlofsky,et al.  Efficient real-time reservoir management using adjoint-based optimal control and model updating , 2006 .

[9]  Muhammad Sahimi,et al.  Computer simulation of gas generation and transport in landfills. V: Use of artificial neural network and the genetic algorithm for short- and long-term forecasting and planning , 2011 .

[10]  Mrinal K. Sen,et al.  On optimization algorithms for the reservoir oil well placement problem , 2006 .

[11]  Marley M. B. R. Vellasco,et al.  Evolutionary Optimization of Smart-Wells Control Under Technical Uncertainties , 2007 .

[12]  Dongxiao Zhang,et al.  Efficient Ensemble-Based Closed-Loop Production Optimization , 2009 .

[13]  Muhammad Sahimi,et al.  Computer simulation of gas generation and transport in landfills II: Dynamic conditions , 2006 .

[14]  K. Aziz,et al.  Optimization of Production Operations in Petroleum Fields , 2002 .

[15]  Dean S. Oliver,et al.  History Matching of Three-Phase Flow Production Data , 2001 .

[16]  T. J. Harding,et al.  Optimisation of Production Strategies using Stochastic Search Methods , 1996 .

[17]  Dean S. Oliver,et al.  An Improved Approach for Ensemble-Based Production Optimization , 2009 .

[18]  Dean S. Oliver,et al.  Conditioning Geostatistical Models to Two-Phase Production Data , 1999 .

[19]  Muhammad Sahimi,et al.  Computer simulation of gas generation and transport in landfills. IV: Modeling of liquid–gas flow , 2010 .

[20]  C. W. Harper,et al.  A FORTRAN IV program for comparing ranking algorithms in quantitative biostratigraphy , 1984 .

[21]  Dean S. Oliver,et al.  Smart Well Production Optimization Using An Ensemble-Based Method , 2010 .

[22]  Paul T. Boggs,et al.  Sequential Quadratic Programming , 1995, Acta Numerica.

[23]  M. L. Wasserman,et al.  A New Algorithm for Automatic History Matching , 1974 .