Insightful heat exchanger network retrofit design using Monte Carlo simulation

The aim of this paper is to use Monte Carlo simulation (MCS) to analyse the effect of stream data variation on the performance of retrofitted heat exchanger networks. MCS offers a stochastic approach to retrofit design analysis by using historical stream data to model how a retrofitted network would perform over time. The method is demonstrated with two case studies. The first is an illustrative example that showcases the features of the proposed method, while the second is a real-life case study using historical data from a section of a Kraft mill's heat exchanger network. The results show how MCS can be used as a comparison tool between exchangers and networks, differentiating between designs with similar steady-state performance, and offering insights into the reliability of the heat exchangers. For example, in the case study, the MCS results show that an estimated 62% of the time, a cold stream will exceed its target temperature under one of the retrofit proposals (which could have serious process operation and safety issues). MCS also offers another way of conducting an economic analysis which has so far tended to be less optimistic than the steady-state analysis and offers greater confidence in the profitability estimates.

[1]  Nathan S. Lal,et al.  Automated retrofit targeting of heat exchanger networks , 2018, Frontiers of Chemical Science and Engineering.

[2]  Robin Smith,et al.  Recent development in the retrofit of heat exchanger networks , 2010 .

[3]  Neven Duić,et al.  Approaches for retrofitting heat exchanger networks within processes and Total Sites , 2019, Journal of Cleaner Production.

[4]  Richard Taylor.  Interpretation of the Correlation Coefficient: A Basic Review , 1990 .

[5]  Matthias Philipp,et al.  Optimal energy supply structures for industrial food processing sites in different countries considering energy transitions , 2017 .

[6]  Anwar Khalil Sheikh,et al.  Uncertainty analysis of heat-exchanger thermal designs using the Monte Carlo simulation technique , 1993 .

[7]  Antonio Campo,et al.  Uncertainty analysis for the experimental estimation of heat transfer correlations combining the Wilson plot method and the Monte Carlo technique , 2018, International Journal of Thermal Sciences.

[8]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[9]  Thore Berntsson,et al.  Energy transfer diagram for site-wide analysis and application to a kraft pulp mill , 2015 .

[10]  Robin Smith,et al.  Cost-effective strategy for heat exchanger network retrofit , 2017 .

[11]  Hao Liu,et al.  Unsteady analysis of a bottoming Organic Rankine Cycle for exhaust heat recovery from an Internal Combustion Engine using Monte Carlo simulation , 2016 .

[12]  Brent R. Young,et al.  A controllability index for heat exchanger networks , 2003 .

[13]  Zdravko Kravanja,et al.  Designing a Total Site for an entire lifetime under fluctuating utility prices , 2015, Comput. Chem. Eng..

[14]  Wallace B. Whiting,et al.  Sensitivity and uncertainty analysis of heat-exchanger designs to physical properties estimation , 2001 .

[15]  Fatma H. Ashour,et al.  Temperature driving force (TDF) curves for heat exchanger network retrofit – A case study and implications , 2017 .

[16]  H. Bozdogan.  Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions , 1987 .

[17]  Nathan S. Lal,et al.  A novel Heat Exchanger Network Bridge Retrofit method using the Modified Energy Transfer Diagram , 2018, Energy.

[18]  Dominic C.Y. Foo,et al.  P-graph and Monte Carlo simulation approach to planning carbon management networks , 2017, Comput. Chem. Eng..

[19]  Nathan S. Lal,et al.  Accounting for stream variability in retrofit problems using Monte Carlo simulation , 2018 .

[20]  Dominic C.Y. Foo,et al.  Assessing the sensitivity of water networks to noisy mass loads using Monte Carlo simulation , 2007, Comput. Chem. Eng..

[21]  Paul Stuart,et al.  New analysis method to reduce the industrial energy requirements by heat-exchanger network retrofit: Part 1 – Concepts , 2017 .

[22]  Robin Smith,et al.  Retrofit of heat exchanger networks with heat transfer enhancement based on an area ratio approach , 2016 .

[23]  Emad A. Mohammed,et al.  Chapter 32 – Emerging Business Intelligence Framework for a Clinical Laboratory Through Big Data Analytics , 2015 .

[24]  Serge Domenech,et al.  A mixed method for retrofiting heat-exchanger networks , 1998 .

[25]  Martin John Atkins,et al.  A derivative based method for cost optimal area allocation in heat exchanger networks , 2014 .

[26]  Paul Stuart,et al.  Linking pinch analysis and bridge analysis to save energy by heat-exchanger network retrofit , 2016 .

[27]  Martin John Atkins,et al.  Ensuring Cost-effective Heat Exchanger Network Design for Non-Continuous Processes , 2012 .

[28]  Raymond R. Tan,et al.  Assessing the Sensitivity of Bioenergy Parks to Capacity Disruptions Using Monte Carlo Simulation , 2017 .

[29]  Pascal Floquet,et al.  Flexibility Assessment of Heat Exchanger Networks: From a Thorough Data Extraction to Robustness Evaluation , 2017 .

[30]  Mogens Weel Hansen,et al.  Heat exchanger network modelling framework for optimal design and retrofitting , 1996 .

[31]  Sharifah Rafidah Wan Alwi,et al.  Simultaneous diagnosis and retrofit of heat exchanger network via individual process stream mapping , 2018, Energy.

[32]  Zdravko Kravanja,et al.  Stochastic Multi-Objective Process Optimization by using the Composite Objective Function , 2017 .

[33]  Robin Smith,et al.  Chemical Process: Design and Integration , 2005 .

[34]  Nathan S. Lal,et al.  Solving complex retrofit problems using constraints and bridge analysis , 2018 .

[35]  Zdravko Kravanja,et al.  A methodology for the synthesis of heat exchanger networks having large numbers of uncertain parameters , 2015 .

[36]  Jiří Jaromír Klemeš,et al.  New directions in the implementation of Pinch Methodology (PM) , 2018, Renewable and Sustainable Energy Reviews.

[37]  H. Barreto,et al.  Introductory Econometrics: Monte Carlo Simulation , 2005 .

[38]  Sharifah Rafidah Wan Alwi,et al.  Process Integration and Intensification: Saving Energy, Water and Resources , 2014 .

[39]  Jin-Kuk Kim,et al.  Availability and reliability considerations in the design and optimisation of flexible utility systems , 2008 .