Fuel Gas Network Synthesis Using Block Superstructure

Fuel gas network (FGN) synthesis is a systematic method for reducing fresh fuel consumption in a chemical plant. In this work, we address FGN synthesis problems using a block superstructure representation that was originally proposed for process design and intensification. The blocks interact with each other through direct flows that connect a block with its adjacent blocks and through jump flows that connect a block with all nonadjacent blocks. The blocks with external feed streams are viewed as fuel sources and the blocks with product streams are regarded as fuel sinks. An additional layer of blocks are added as pools when there exists intermediate operations among source and sink blocks. These blocks can be arranged in a I × J two-dimensional grid with I = 1 for problems without pools, or I = 2 for problems with pools. J is determined by the maximum number of pools/sinks. With this representation, we formulate FGN synthesis problem as a mixed-integer nonlinear (MINLP) formulation to optimally design a fuel gas network with minimal total annual cost. We revisit a literature case study on LNG plants to demonstrate the capability of the proposed approach.

[1]  Reinhard Radermacher,et al.  Optimization of propane pre-cooled mixed refrigerant LNG plant , 2011 .

[2]  R. Sargent,et al.  A general algorithm for short-term scheduling of batch operations */I , 1993 .

[3]  Efstratios N. Pistikopoulos,et al.  Generalized modular framework for the synthesis of heat integrated distillation column sequences , 2005 .

[4]  Christos T. Maravelias,et al.  A superstructure-based framework for bio-separation network synthesis , 2017, Comput. Chem. Eng..

[5]  Miguel J. Bagajewicz,et al.  Mass/heat‐exchange network representation of distillation networks , 1992 .

[6]  Rafiqul Gani,et al.  Phenomena Based Methodology for Process Synthesis Incorporating Process Intensification , 2013 .

[7]  M. M. Faruque Hasan,et al.  Simultaneous Process Synthesis and Process Intensification using Building Blocks , 2017 .

[8]  Gang Rong,et al.  An MILP model for multi-period optimization of fuel gas system scheduling in refinery and its marginal value analysis , 2008 .

[9]  O. S. Ismail,et al.  Global Impact of Gas Flaring , 2012 .

[10]  Efstratios N. Pistikopoulos,et al.  Generalized modular representation framework for process synthesis , 1996 .

[11]  M. Gholami,et al.  Reducing Energy Consumption and GHG Emission by Integration of Flare Gas with Fuel Gas Network in Refinery , 2014 .

[12]  Rafiqul Gani,et al.  Process synthesis, design and analysis using a process-group contribution method , 2015, Comput. Chem. Eng..

[13]  Weifeng Hou,et al.  Simulation based approach for optimal scheduling of fuel gas system in refinery , 2010 .

[14]  Douglas C. White.  Advanced automation technology reduces refinery energy costs , 2005 .

[15]  Majid Gholami,et al.  Integration of flare gas with fuel gas network in refineries , 2016 .

[16]  Ferenc Friedler,et al.  Process integration, modelling and optimisation for energy saving and pollution reduction , 2009 .

[17]  Jianping Li,et al.  A General Framework for Process Synthesis, Integration and Intensification , 2019 .

[18]  A. MacKenzie,et al.  Gas flaring and resultant air pollution: A review focusing on black carbon. , 2016, Environmental pollution.

[19]  Miguel J. Bagajewicz,et al.  On the state space approach to mass/heat exchanger network design* , 1998 .

[20]  L. T. Fan,et al.  Graph-theoretic approach to process synthesis: Polynomial algorithm for maximal structure generation , 1993 .

[21]  Esmail M. A. Mokheimer,et al.  Optimal integration of solar energy with fossil fuel gas turbine cogeneration plants using three different CSP technologies in Saudi Arabia , 2017 .

[22]  L. T. Fan,et al.  Graph-theoretic approach to process synthesis: axioms and theorems , 1992 .

[23]  I. Moon,et al.  Current Status and Perspectives of Liquefied Natural Gas (LNG) Plant Design , 2013 .

[24]  Chunfei Wu,et al.  Utilization of NiO/porous ceramic monolithic catalyst for upgrading biomass fuel gas , 2017, Journal of the Energy Institute.

[25]  Rafiqul Gani,et al.  Sustainable process synthesis-intensification , 2015, Comput. Chem. Eng..

[27]  Mahmoud M. El-Halwagi,et al.  Pollution prevention through process integration , 1997 .

[28]  Rafiqul Gani,et al.  Process intensification: A perspective on process synthesis , 2010 .

[29]  A. De Carli,et al.  Intelligent management and control of fuel gas network , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[30]  Qi Chen,et al.  Recent Developments and Challenges in Optimization-Based Process Synthesis. , 2017, Annual review of chemical and biomolecular engineering.

[31]  Jianping Li,et al.  Process synthesis using block superstructure with automated flowsheet generation and optimization , 2018, AIChE Journal.

[32]  Iftekhar A. Karimi,et al.  Minimize Flaring through Integration with Fuel Gas Networks , 2012 .

[33]  X. Zhu,et al.  A Simultaneous Optimization Strategy for Overall Integration in Refinery Planning , 2001 .

[34]  Yongrong Yang,et al.  Energy configuration and operation optimization of refinery fuel gas networks , 2015 .

[35]  Iftekhar A. Karimi,et al.  Preliminary Synthesis of Fuel Gas Networks to Conserve Energy and Preserve the Environment , 2011 .

[36]  Jianping Li,et al.  Systematic process intensification using building blocks , 2017, Comput. Chem. Eng..

[37]  I. Grossmann,et al.  A systematic modeling framework of superstructure optimization in process synthesis , 1999 .

[38]  Christodoulos A. Floudas,et al.  ANTIGONE: Algorithms for coNTinuous / Integer Global Optimization of Nonlinear Equations , 2014, Journal of Global Optimization.

[39]  Christos T. Maravelias,et al.  A superstructure representation, generation, and modeling framework for chemical process synthesis , 2016 .