A novel methodology for the design of waste heat recovery network in eco-industrial park using techno-economic analysis and multi-objective optimization

Zhang, Chuan; Zhou, Li; Chhabra, Pulkit; Garud, Sushant S.; Aditya, Kevin; Romagnoli, Alessandro; Comodi, Gabriele; Magro, Fabio Dal; Meneghetti, Antonella; Kraft, Markus

doi:10.1016/j.apenergy.2016.10.016

Cited by 46 publications

(19 citation statements)

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“…This however, is at the expense of the amount of waste heat recovered, and in turn, in the amount of savings achieved, if one was to also compare these clusters with that of cluster 1 in Figure 9b, consisting of solutions with larger tank sizes. This similar observation was made in [7], whereby, if payback period was a priority, the solution will come at the expense of its efficiency and effectiveness of the waste heat recovery. If a small tank is, however, required, solutions in cluster 4 provide a better investment in comparison to those in cluster 3.…”

Section: Clustering In the Objective Spacementioning

confidence: 84%

“…Because of this, they have become increasingly popular in the design of engineering systems [3], [4], [5], [6], including the optimisation of energy systems. Examples are described in [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

“…A common method of visualising the results is to use a scatter plot, or decision maps, as shown in [14], [10], [13], but in general these can only show three objectives or parameters at once, which is limiting. Furthermore, the correlation between parameter and objective values are not explicitly shown, and in practice, analysis of the correlation is only applied to a selected few solutions, as depicted in [7], [8], [9]. Typically, they are the solutions at the extrema of the Pareto-optimal set, giving limited insight to the attributes of the other Pareto-optimal solutions found.…”

Section: Introductionmentioning

confidence: 99%

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Exploring multi-objective trade-offs in the design space of a waste heat recovery system

et al. 2017

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A waste heat recovery system (WHRS) is used to capture waste heat released from an industrial process, and transform the heat into reusable energy.In practice, it can be difficult to identify the optimal form of a WHRS for a particular installation, since this can depend on various design objectives, which are often mutually exclusive. More so when the number of objectives is large.To address this problem, a multiobjective evolutionary algorithm (MOEA) was used to explore and characterise the trade-off surface within the design space of a particular WHRS. A combination of clustering algorithm and parallel coordinates plots was proposed for use in analysing the results. The trade-off surface is first segmented using a clustering algorithm and parallel coordinates plots are then used to both visualise and understand the resulting set of Pareto-optimal * Corresponding author Email address: M.Mokhtar@napier.ac.uk (Maizura Mokhtar) February 20, 2017 designs. As a case study, a simulation of a WHRS commonly found in the food and drinks process industries was developed, comprising of a desuperheater coupled to a hot water reservoir. The system was parameterised, considering typical objectives, and the MOEA used to build a library of alternative Pareto-optimal designs that can be used by installers. The resulting visualisation are used to better understand the sensitivity of the system's parameters and their trade-offs, providing another source of information for prospective installations. Preprint submitted to Applied Energy

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Section: Clustering In the Objective Spacementioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring multi-objective trade-offs in the design space of a waste heat recovery system

et al. 2017

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“…In an eco-industrial park context, optimisation approaches developed to determine optimal resource flows are discussed in [13], with the incorporation of uncertainty on performance carried out in [14]. From a resource recovery perspective, heat recovery networks are considered in [15] for example.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of wastewater treatment strategies in eco-industrial parks: Technology, location and transport

O’Dwyer

Chen

Wang

et al. 2020

Chemical Engineering Journal

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The expanding population and rapid urbanisation, in particular in the Global South, are leading to global challenges on resource supply stress and rising waste generation. A transformation to resource-circular systems and sustainable recovery of carbon-containing and nutrient-rich waste offers a way to tackle such challenges. Eco-industrial parks have the potential to capture symbioses across individual waste producers, leading to more effective waste-recovery schemes. With whole-system design, economically attractive approaches can be achieved, reducing the environmental impacts while increasing the recovery of high-value resources. In this paper, an optimisation framework is developed to enable such design, allowing for wide ranging treatment options to be considered capturing both technological and financial detail. As well as technology selection, the framework also accounts for spatial aspects, with the design of suitable transport networks playing a key role. A range of scenarios are investigated using the network, highlighting the multi-faceted nature of the problem. The need to incorporate the impact of resource recovery at the design stage is shown to be of particular importance.

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“…The prosperous development of data science and ML in recent years has brought new opportunities to many traditional industries; the energy industry could be and should be one of the beneficiaries. The potential benefits of going digital are being actively explored by government, industry, and academia from various perspectives, ranging from power generation unit fault diagnosis (Ajami and Daneshvar, 2012) to inter-plant network synthesis in eco-industrial parks (Zhang et al, 2016). The recent IEA report points out that "digitally interconnected systems could fundamentally transform the current energy industry" (International Energy Agency, 2016); the newly launched US Department of Energy's Clean Energy Smart Manufacturing Innovation Institute (CESMII) also supports the future integration of smart manufacturing and energy industry, of which one important aspect is exploring the possibility of using smart manufacturing conceptions to improve efficiency and sustainability of energy industry (Edgar and Pistikopoulos, 2018).…”

Section: Machine Learning Fundamentalsmentioning

confidence: 99%

Data driven modeling and optimization of energy systems

Zhang¹

Self Cite

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Firstly I would like to acknowledge my supervisors, Prof. Alessandro Romagnoli in Nanyang Technological University (NTU) Singapore and Prof. Markus Kraft in Cambridge UK. Thanks to them for accepting me as PhD student and funding me for my study, without this all following stories would not exist. Prof Alessandro is a patient supervisor and mentor; he spent his time listening to my presentations, amending my papers, and improving my English. He has clear logic and good explanation ability, which are extremely helpful when I am lost in research. Prof Markus is equally indispensable for my achievements, lots of my research ideas are inspired by him. His erudite and sharp mind is an academic role model for me, it is also worth mentioning that he devoted a significant portion of his precious time to me, which makes me feel lucky. I would like to acknowledge my parents again here. My mother suffered from stroke during the second year of my PhD, we really had terrible days, but she is physically and mentally strong enough to let her fully recover now. Her determination in fighting against disease is a true family spirit that must be passed on to me. My father is a untalkative person but I know his love and support are always there when I need them. I greatly appreciate my girlfriend's company as well, her sunny attitude towards life lights me up during down times, I hope she enjoys the remaining of her PhD and finish smoothly.

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A novel methodology for the design of waste heat recovery network in eco-industrial park using techno-economic analysis and multi-objective optimization

Cited by 46 publications

References 23 publications

Exploring multi-objective trade-offs in the design space of a waste heat recovery system

Exploring multi-objective trade-offs in the design space of a waste heat recovery system

Optimisation of wastewater treatment strategies in eco-industrial parks: Technology, location and transport

Data driven modeling and optimization of energy systems

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