Network Modeling and Design for Low Grade Heat Recovery, Refrigeration, and Utilization in Industrial Parks

Zhang, B. J.; Zhang, Z. L.; Liu, K.; Chen, Q. L.

doi:10.1021/acs.iecr.6b02033

Cited by 14 publications

(9 citation statements)

References 45 publications

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“…This paper addresses the gaps denoted (2) and (3) by presenting a novel comprehensive superstructure synthesis method which is solved using mathematical programming for optimal integration of industrial heat pump systems. Preliminary versions of this heat pump superstructure (HPS) [32,33] were generalized and extended to incorporate fluid selection, HEN [34] 2016 AHP MINLP -contin. 1 TAC G Oluleye et al [35] 2016 HP/AHP/HE/AHT TP HYSYS 10 K 2 Fuel S TP Oluleye et al [36] 2016 HP/AHP/HE/AHT MILP HYSYS 10 K 2 TAC S Binary Dinh et al [37] 2015 HP MILP PR fixed 3 Power S ( ) Flowrates Kamalinejad et al [29] 2015 HP MINLP RP contin.…”

Section: Discussion and Goalmentioning

confidence: 99%

Optimal heat pump integration in industrial processes

et al. 2018

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Among the options for industrial waste heat recovery and reuse which are currently discussed, heat pumping receives far less attention than other technologies (e.g. organic rankine cycles). This, in particular, can be linked to a lack of comprehensive methods for optimal design of industrial heat pump and refrigeration systems, which must take into account technical insights, mathematical principles and state-of-the-art features. Such methods could serve in a twofold manner: (1) in providing a foundation for analysis of heat pump economic and energetic saving potentials in different industries, and further (2) in giving directions for experimentalists and equipment manufacturers to adapt and develop heat pump equipment to better fit the process needs. This work presents a novel heat pump synthesis method embedded in a computational framework to provide a basis for such analysis. The superstructure-based approach is solved in a decomposition solution strategy based on mathematical programming. Heat pump features are incorporated in a comprehensive way while considering technical limitations and providing a set of solutions to allow expert-based decision making at the final stage. Benchmarking is completed by applying the method on a set of literature cases which yields improved-cost solutions between 5 and 30% compared to those reported previously. An extended version of one case is presented considering fluid selection, heat exchanger network (HEN) cost estimations, and technical constraints. The extended case highlights a trade-off between energy efficiency and system complexity expressed in number of compression stages, gas-and sub-cooling. This is especially evident when comparing the solutions with 3 and 5 compression stages causing an increase of the coefficient of performance (COP) from 2.9 to 3.1 at 3% increase in total annualized costs (TAC).

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Section: Discussion and Goalmentioning

confidence: 99%

Optimal heat pump integration in industrial processes

et al. 2018

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“…The constraint for refrigeration is represented as eq , in which Q k cd is the cold energy demand by sink k . The detailed models of the four parts can be found in our previous publication …”

Section: Mathematical Modelingmentioning

confidence: 99%

“…These four parts are effectively coupled using the energy flow variables and parameters in the optimization framework. However, previous studies involved only parts of the four elements and did not fully integrate them. , The augmented ε-constraint method is applied in this study. Mavrotas and Florios , performed the lexicographic optimization on the objective functions to construct the payoff table with only Pareto optimal solutions.…”

Section: Mathematical Frameworkmentioning

confidence: 99%

“…Hammond and Norman used absorption chillers to convert LGE to cold energy to recover surplus heat. Zhang et al proposed a MINLP model to optimize the energy and economic performance of the entire network and simultaneously integrate the LGE recovery, refrigeration, and utilization in industrial parks. Salmi et al presented a model to optimize an absorption refrigeration system using WE from ships as the energy source.…”

Section: Introductionmentioning

confidence: 99%

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Sustainable Retrofit Design of Natural Gas Power Plants for Low-Grade Energy Recovery

Tang

Chen

et al. 2019

Ind. Eng. Chem. Res.

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Large amounts of low-grade energy in natural gas power plants are not being efficiently recovered or used. They are often emitted directly to the environment. It is quite challenging to retrofit natural gas power plants for the integration of low-grade energy recovery and further optimize power generation, production and supply of hot and cold energy, and pipeline networks for cold energy distribution as a total site. In this study, we present a process superstructure for the sustainable retrofit of natural gas power plants to recover low-grade energy and deliver it to different users. The waste energy sources include cold energy from liquefied natural gas, pressure energy from pipeline natural gas streams, and lowgrade heat from low-pressure steam in power plants. A mixed integer nonlinear programming framework is formulated to optimize the process structure to minimize the total annual cost and maximize the exergy efficiency to obtain the best sustainable retrofit process for low-grade energy recovery. The optimization framework includes models of the existing gas turbines and boilers and the new pipeline network and refrigeration system. The mathematical framework is applied to a real industrial example, and the power consumption for cold energy production is significantly reduced by 32%. The ratio of different energy sources and the distance of cold energy distribution are investigated, and different optimization results are analyzed. Finally, exergy analysis is suggested to evaluate the sustainability of the retrofit. According to the results of the exergy analysis, the total exergy efficiency under the minimum total annual cost is increased by 6.172%.

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“…For example, in [5], optimal location of the intermediate-fluid circuits was determined for heat integration across multiple plants to maximize savings. Some studies have focused specifically on design optimization in industrial clusters [6][7][8][9]. However, even for plants with a well-designed surplus heat recovery system, there may be further operational and control challenges to deal with.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Robust Optimal Operation of Thermal Energy Storage in Industrial Clusters

2020

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Industrial waste heat recovery is an attractive option having the simultaneous benefits of reducing energy costs as well as carbon emissions. In this context, thermal energy storage can be used along with an optimal operation strategy like model predictive control (MPC) to realize significant energy savings. However, conventional control methods offer little robustness against uncertainty in terms of daily operation, where supply and demand of energy in the cluster can vary significantly from their predicted profiles. A major concern is that ignoring the uncertainties in the system may lead to the system violating critical constraints that affect the quality of the end-product of the participating processes. To this end, we present a method to make optimal energy storage and discharge decisions, while rigorously handling this uncertainty. We employ multivariate data analysis on historical industrial data to implement a multistage nonlinear MPC scheme based on a scenario-tree formulation, where the economic objective is to minimize energy costs. Principal component analysis (PCA) is used to detect outliers in the industrial data on heat profiles, and to select appropriate scenarios for building the scenario-tree in the multistage MPC formulation. The results show that this data-driven robust MPC approach is successfully able to keep the system from violating any operating constraints. The solutions obtained are not overly conservative, even in the presence of significant deviations between the predicted and actual heat profiles. This leads to an energy-efficient utilization of the storage unit, benefiting all the stakeholders involved in heat-exchange in the cluster.

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Network Modeling and Design for Low Grade Heat Recovery, Refrigeration, and Utilization in Industrial Parks

Cited by 14 publications

References 45 publications

Optimal heat pump integration in industrial processes

Optimal heat pump integration in industrial processes

Sustainable Retrofit Design of Natural Gas Power Plants for Low-Grade Energy Recovery

Data-Driven Robust Optimal Operation of Thermal Energy Storage in Industrial Clusters

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