An equation-oriented approach for handling thermodynamics based on cubic equation of state in process optimization

A new equation-oriented process model for multistream heat exchangers (MHEX) is presented with a special emphasis on handling phase changes. The model internally uses the pinch concept to ensure the minimum driving force criteria. Streams capable of phase change are split into substreams corresponding to each of the phases. A novel disjunctive representation is proposed that identifies the phases traversed by a stream during heat exchange and assigns appropriate heat loads and temperatures for heat integration. The disjunctive model can be reformulated to avoid Boolean (or integer) variables using inner minimization and complementarity constraints. The model is suitable for optimization studies, particularly when the phases of the streams at the entry and exit of the MHEX are not known a priori. The capability of the model is illustrated using two case studies based on cryogenic applications. V V C 2011 American Institute of Chemical Engineers AIChE J, 58: 190-204, 2012 Keywords: heat transfer, mathematical modeling, optimization IntroductionHeat integration in the chemical process industry is usually performed by a sequential strategy. The first step in this strategy is to design and optimize the process while assuming that all the heating and cooling loads will be supplied by the utilities. Once the process conditions (pressure, temperature, and flowrates of streams) are known, heat integration can be performed in the subsequent step, using techniques such as the problem table 1 or LP/MILP transshipment model. 2 The literature also suggests an alternate simultaneous strategy that performs the heat integration while optimizing the process. 3,4 Although the simultaneous strategy is much more difficult to implement and solve, it can lead to larger economic benefits. 5A multistream heat exchanger (MHEX) is a single process unit in which multiple hot and cold streams exchange heat simultaneously. MHEXs are very common in cryogenic applications where heat transfer equipment need to be kept compact and well-insulated while recovering heat from streams at very small temperature driving forces. 6 Use of a MHEX to perform such heat transfer tasks often leads to substantial savings in both energy and capital cost. MHEXs are traditionally analyzed using composite curves, a thermodynamic concept used in heat integration called pinch analysis. The streams in an MHEX are multicomponent and typically undergo phase changes. An important issue concerning the use of the pinch concept (or heat integration) for design or optimization of MHEXs is how to handle the nonlinear variation in heat capacity-flowrates when a stream changes phase while exchanging heat, particularly when the phases are not known a priori.There are a few noteworthy contributions on handling streams with phase changes. Ponce-Ortega et al. 7 proposed a new approximation to the logarithmic mean temperature difference, which handles matches involving phase changes in the heat exchanger network. Their approach assumes Correspondence concerning this article shoul...

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“…We refer to Kamath et al, 24 who describe an equation-based optimization model to accomplish this objective.…”

Section: Industrial Case Study: Natural Gas Liquefactionmentioning

confidence: 99%

Modeling multistream heat exchangers with and without phase changes for simultaneous optimization and heat integration

2011

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“…However, testing each of the reformulations in turn in a simple optimization shows despite the heightened non-linearity, the avoidance of nondifferentiabilities for values close to zero is computationally of advantage. To further stabilize the computation, Kamath and Biegler et al's conditions to distinguish between vapor and liquid roots of cubic equations of state are employed as inequality constraints (Kamath et al, 2010) as stated in Eqs. (5) and (6).…”

Section: Membrane Modelsmentioning

confidence: 99%

Optimization during the process synthesis: enabling the oxidative coupling of methane by minimizing the energy required for the carbon dioxide removal

Esche

Müller

Song

et al. 2015

Journal of Cleaner Production

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“…They are in principle classified into two groups, nonlinear optimization techniques and stochastic optimization methods. Using the first techniques, like sequential quadratic programming (SQP) or multiple shooting approaches, only local convergence can be guaranteed [2,3,4]. Therefore, in practical applications, the second group, the stochastic optimization methods, are proposed for the solution of optimization problems in chemical process design [5,6], for they can found the global minimum.…”

Section: Introductionmentioning

confidence: 99%

Optimal operation strategies for batch distillation by using a fast adaptive simulated annealing algorithm

Wang

2012

Proceedings of the 10th World Congress on Intelligent Control and Automation

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Batch distillation processes are widely used in the chemical industry. In this work, the optimal operation strategies for such processes are studied by using a fast adaptive simulated annealing (FASA) algorithm. Simulated annealing algorithm is stochastic in nature, and it converges towards a global optimum. However, its computational load is usually much too heavy. In this study, a FASA algorithm was presented with fast and adaptive moves in the searched neighborhood range to decrease the computation load. According to the characteristics of batch distillation process, a FASA-based parallelized optimization computation approach was proposed and then it was applied to a model of a batch distillation plant. The optimal operation strategies with respect to minimal production time and maximal profit were studied. The results show the effectiveness of the method.

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An equation-oriented approach for handling thermodynamics based on cubic equation of state in process optimization

Cited by 82 publications

References 26 publications

Modeling multistream heat exchangers with and without phase changes for simultaneous optimization and heat integration

Modeling multistream heat exchangers with and without phase changes for simultaneous optimization and heat integration

Optimization during the process synthesis: enabling the oxidative coupling of methane by minimizing the energy required for the carbon dioxide removal

Optimal operation strategies for batch distillation by using a fast adaptive simulated annealing algorithm

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