Pressure buffering control to reduce pollution and improve flow stability in industrial gas headers

Abstract This paper describes various regulatory and advanced control schemes which can be applied to industrial gas headers. The intention is to exploit the buffering capacity for pollution control as well as improve flow stability for consumers. The control schemes are compared using a Monte Carlo simulation on a simulated case study and a sensitivity analysis is done to evaluate the impact of variations in the gas properties on the cost functions. A compensated linear model predictive controller (CLMPC) is implemented on a real industrial header and compared with standard proportional–integral (PI) control. It is found that gas emissions and consumer stability can be substantially improved by intelligently utilising the available pressure buffering capacity in industrial gas headers.

[2]  Prakash Krishnaswami,et al.  Transient Optimization in Natural Gas Compressor Stations for Linepack Operation , 2007 .

[3]  Cheng-Ching Yu,et al.  A two degree of freedom level control , 2001 .

[4]  B. Likozar,et al.  A review of plasma-assisted catalytic conversion of gaseous carbon dioxide and methane into value-added platform chemicals and fuels , 2018, RSC advances.

[5]  Daniel Sbarbaro,et al.  Averaging level control: An approach based on mass balance , 2007 .

[6]  Tak-Fai Cheung Liquid-Level Control in Single Tanks and Cascades of Tanks with Proportional-Only and Proportional-Integral Feedback Controllers , 1979 .

[7]  Michael King,et al.  Process Control: A Practical Approach , 2011 .

[8]  Arno de Klerk,et al.  Fischer-Tropsch Refining , 2011 .

[9]  Krister Forsman,et al.  Practical Control of Surge Tanks Suffering from Frequent Inlet Flow Upsets , 2012 .

[10]  Paola Annoni,et al.  Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..

[11]  Richard D. Braatz,et al.  Averaging Level Control to Reduce Off-Spec Material in a Continuous Pharmaceutical Pilot Plant , 2013 .

[12]  P. Djavdan,et al.  Model Predictive Averaging Level Control using Disturbance Prediction , 1995 .

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

[14]  Thomas F. Edgar,et al.  Process Dynamics and Control , 1989 .

[15]  H. Atashi,et al.  Green fuel from coal via Fischer–Tropsch process: scenario of optimal condition of process and modelling , 2018 .

[16]  Ian K. Craig,et al.  Pressure measurement location selection in industrial gas headers for buffering control , 2021, Comput. Chem. Eng..

[17]  Sigurd Skogestad,et al.  Improved PI control for a surge tank satisfying level constraints , 2018 .

[18]  M. Morari,et al.  On-line optimization of gas pipeline networks , 1988, Autom..

[19]  T. Sharma,et al.  Effect of operating parameters on coal gasification , 2018 .

[20]  Roberto Sanchis,et al.  A new approach to averaging level control , 2011 .

[21]  C. Sattler,et al.  High temperature production of hydrogen: Assessment of non-renewable resources technologies and emerging trends , 2020 .

[22]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[23]  A. J. Taylor,et al.  Optimize surge vessel control Proportional only algorithm complements multivariable predictive control Instrumentation and Control , 2002 .

[24]  Krister Forsman,et al.  Performance Analysis of Robust Averaging Level Control , 2011 .

[25]  Sigurd Skogestad,et al.  Simple analytic rules for model reduction and PID controller tuning , 2003 .

[26]  Tatjana Stykel,et al.  Model Order Reduction for Differential-Algebraic Equations: A Survey , 2017 .

[27]  Ian K. Craig,et al.  Modelling of methane-rich gas pipeline networks for simulation and control , 2020 .

[28]  Sigurd Skogestad,et al.  Optimal operation of oil and gas production using simple feedback control structures , 2019, Control Engineering Practice.

[29]  F. G. Greg Shinskey,et al.  Process Control Systems: Application, Design and Tuning , 1990 .

[30]  B. Enger,et al.  Fischer–Tropsch conversion of biomass‐derived synthesis gas to liquid fuels , 2013 .

[31]  Eric C. D. Tan,et al.  Economic and environmental potentials for natural gas to enhance biomass-to-liquid fuels technologies , 2018 .

[32]  Moonyong Lee,et al.  CONSTRAINED OPTIMAL CONTROL OF LIQUID LEVEL LOOP USING A CONVENTIONAL PROPORTIONAL-INTEGRAL CONTROLLER , 2009 .

[33]  William L. Luyben,et al.  Nonlinear and Nonconventional Liquid Level Controllers , 1980 .

[34]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[35]  Xiao Jiang,et al.  Direct Transformation of Carbon Dioxide to Value-Added Hydrocarbons by Physical Mixtures of Fe5C2 and K-Modified Al2O3 , 2018, Industrial & Engineering Chemistry Research.

[36]  Manfred Morari,et al.  Model predictive optimal averaging level control , 1989 .

[37]  Ian K. Craig,et al.  Fuel gas blending benchmark for economic performance evaluation of advanced control and state estimation , 2012 .

[38]  Environment, social, and governance (ESG) criteria and preference of managers , 2014 .

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

[40]  H. Egger,et al.  Maximizing the storage capacity of gas networks: a global MINLP approach , 2018, Optimization and Engineering.

[41]  Michael J. Piovoso,et al.  Optimal averaging level control for the tennessee eastman problem , 1995 .

[42]  Ian K. Craig,et al.  Modelling, validation, and control of an industrial fuel gas blending system☆ , 2011 .

[43]  Sigurd Skogestad,et al.  Multi-input single-output control for extending the operating range: Generalized split range control using the baton strategy , 2020, Journal of Process Control.