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Reducing Energy Demand in the Industrial and Manufacturing Sectors

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (15 July 2018) | Viewed by 7310

Special Issue Editors

Complex Systems Research Centre, School of Management, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK
Interests: multi-carrier energy network; zone method based radiation analysis; carbon capture, utilization, and storage (CCUS); model predictive control
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Guest Editor
Lecturer in Control and Power Group, Department of Electrical and Electronic Engineering Imperial College London, London SW7 2AZ, UK
Interests: power system operation and planning; integration of low carbon energy; demand side response; decision making under uncertainty

Special Issue Information

Dear Colleagues,

Globally, industrial energy use is responsible for 33% of greenhouse gas emissions. Rising concern about reducing both energy demand and CO2 emissions is pushing more efforts to achieve sustainable development in energy intensive industries, such as CO2 capture and storage, improving energy efficiency, introducing renewable energy. However, step change in energy intensive industries is still facing many fundamental research and techno-economic challenges. Therefore, technologies and energy integration and optimisation approaches that are applicable to most energy intensive industrial sectors and offer the greatest potential for energy demand reduction and decarbonisation need further attention.

This Special Issue will provide a platform for presenting the novel technologies and energy integration and optimisation approaches that help to reduce energy demand in the industrial and manufacturing sectors. We welcome research papers about theoretical, modelling, experimental and/or data based analysis, as well as review papers, that provide critical overview on the step change of conventional energy systems. This special issue is open to multilevel energy optimisation and management in industries: i) individual process or plant level; ii) integration and optimisation of energy flows in industrial park or region level; iii) interactions with national energy supply systems (grid electricity and gas) etc.

Topics of interest of this Special Issue include, but are not limited to:

  • Emerging energy efficiency technologies and measures
  • Utilisation of bioenergy/waste in industry
  • Acquisition and analysis of large energy data
  • Complex energy network in large industrial manufacturing facilities
  • Business models of future energy markets
  • Model predictive control of energy intensive processes
  • Low carbon manufacturing
  • Carbon market and policy

Dr. Yukun Hu
Assoc. Prof. Dr. Rui Xiong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Energy Efficiency
  • Energy Management
  • Energy Data
  • Complex Systems
  • Business Models
  • Low Carbon Manufacturing
  • Renewable Energy
  • Carbon Capture, Utilization, and Storage (CCUS)
  • Smart Grid

Published Papers (1 paper)

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Research

24 pages, 5975 KiB  
Article
Fuzzy Nonlinear Dynamic Evaporator Model in Supercritical Organic Rankine Cycle Waste Heat Recovery Systems
by Jahedul Islam Chowdhury, Bao Kha Nguyen, David Thornhill, Yukun Hu, Payam Soulatiantork, Nazmiye Balta-Ozkan and Liz Varga
Energies 2018, 11(4), 901; https://doi.org/10.3390/en11040901 - 11 Apr 2018
Cited by 15 | Viewed by 6239
Abstract
The organic Rankine cycle (ORC)-based waste heat recovery (WHR) system operating under a supercritical condition has a higher potential of thermal efficiency and work output than a traditional subcritical cycle. However, the operation of supercritical cycles is more challenging due to the high [...] Read more.
The organic Rankine cycle (ORC)-based waste heat recovery (WHR) system operating under a supercritical condition has a higher potential of thermal efficiency and work output than a traditional subcritical cycle. However, the operation of supercritical cycles is more challenging due to the high pressure in the system and transient behavior of waste heat sources from industrial and automotive engines that affect the performance of the system and the evaporator, which is the most crucial component of the ORC. To take the transient behavior into account, the dynamic model of the evaporator using renowned finite volume (FV) technique is developed in this paper. Although the FV model can capture the transient effects accurately, the model has a limitation for real-time control applications due to its time-intensive computation. To capture the transient effects and reduce the simulation time, a novel fuzzy-based nonlinear dynamic evaporator model is also developed and presented in this paper. The results show that the fuzzy-based model was able to capture the transient effects at a data fitness of over 90%, while it has potential to complete the simulation 700 times faster than the FV model. By integrating with other subcomponent models of the system, such as pump, expander, and condenser, the predicted system output and pressure have a mean average percentage error of 3.11% and 0.001%, respectively. These results suggest that the developed fuzzy-based evaporator and the overall ORC-WHR system can be used for transient simulations and to develop control strategies for real-time applications. Full article
(This article belongs to the Special Issue Reducing Energy Demand in the Industrial and Manufacturing Sectors)
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