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Energies
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Energy Efficiency Improvement in Process Industries
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Energy Efficiency Improvement in Process Industries

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B: Energy and Environment".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 3958

Special Issue Editors

Prof. Dr. Silvia Maria Zanoli

E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, 60131 Ancona, AN, Italy
Interests: advanced process control; automation; model predictive control; petri nets; discrete event systems (DESs); process modelling; energy efficiency; steel industries; cement industries; water distribution networks; hydroelectric power plants; district heating; HVAC; process control; process monitoring; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Crescenzo Pepe

E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, 60131 Ancona, AN, Italy
Interests: advanced process control; model predictive control; process modelling; automation; energy efficiency; steel industries; cement industries; water distribution networks; hydroelectric power plants; district heating; HVAC; process control; process monitoring; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current energy transition, digital transition and geopolitical situation represent significant challenges for researchers, engineers and practitioners. In this context, in order to target the energy goals defined for 2030 and 2050, energy efficiency improvement in the process industries represents a strategic driver. Energy efficiency improvement case studies can be related to software, hardware or hybrid solutions. Theoretical and practical studies are a fundamental requirement in providing universal, robust and holistic tools. Both field and simulation applications represent the present and future for successful projects. Tailored key performance indicators can be formulated for energy efficiency improvement evaluation and certification; for this purpose, data analysis, Industry 4.0 and digital twins can be exploited.

This Special Issue aims to collect contributions related to energy efficiency improvement in the process industries in order to provide an overview of the state-of-the-art of emerging technologies and best practices.

Topics of interest for publication include, but are not limited to:

  • Energy efficiency improvement;
  • Energy efficiency evaluation;
  • Energy efficiency certification;
  • Energy transition;
  • Project feasibility study;
  • Project benefit study;
  • Industrial automation;
  • Energy-intensive processes;
  • Manufacturing processes;
  • Supply chains;
  • Process estimation and control;
  • Optimal control;
  • Predictive control;
  • Real-time optimization;
  • Decision support systems;
  • Revamping;
  • Software upgrade;
  • Hardware upgrade;
  • Steel industry;
  • Cement industry.

Prof. Dr. Silvia Maria Zanoli
Dr. Crescenzo Pepe
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

  • advanced process control
  • model predictive control
  • process optimization
  • process monitoring
  • decision support system
  • industry 4.0
  • process simulation
  • process modellization
  • data analysis
  • digital twin
  • key performance indicator
  • revamping
  • commissioning
  • project management
 

Published Papers (3 papers)

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Research

33 pages, 6329 KiB  
Article
Prediction Modeling of Flue Gas Control for Combustion Efficiency Optimization for Steel Mill Power Plant Boilers Based on Partial Least Squares Regression (PLSR)
by Sang-Mok Lee, So-Won Choi and Eul-Bum Lee
Energies 2023, 16(19), 6907; https://doi.org/10.3390/en16196907 - 30 Sep 2023
Viewed by 1091
Abstract
The energy-intensive steel industry, which consumes substantial amounts of electricity, meets its power demands through external electricity purchases and self-generation through the operation of its own generators. This study aimed to optimize boiler combustion efficiency and increase power generation output by deriving optimal [...] Read more.
The energy-intensive steel industry, which consumes substantial amounts of electricity, meets its power demands through external electricity purchases and self-generation through the operation of its own generators. This study aimed to optimize boiler combustion efficiency and increase power generation output by deriving optimal operational values for O2 and CO within the boiler flue gas using machine learning (ML) with the aim of achieving maximum boiler efficiency. This study focuses on the power-generation boilers at steel mill P in Korea. First, 361 types of operation data from power generation equipment were collected and preprocessed. Subsequently, a partial least squares regression (PLSR) algorithm was used to develop a prediction model for O2 and CO values, known as the Boiler Flue Gas Prediction Model (BFG-PM). The prediction accuracy for O2 was notably high (83.2%), whereas that for CO was lower (53.4%). Nonetheless, the model’s reliability was high because more than 90% of the predicted values were within a 10% error range. Finally, the correlation of the BFG-PM model was applied to the performance test code (PTC) 4.0 for the boiler efficiency calculations formula, deriving the optimal O2 and CO control points. Through a simulation, it was verified that the boiler efficiency was improved by controlling the combustion air. In addition, an average increase in boiler efficiency of 0.29% was confirmed by applying it directly to the generator operating on-site. The results of this study are expected to contribute to annual cost savings, with a reduction of USD 217,000 in electricity purchasing costs and USD 19,700 in greenhouse gas emissions trading expenses. Full article
(This article belongs to the Special Issue Energy Efficiency Improvement in Process Industries)
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20 pages, 11602 KiB  
Article
Synergic Combination of Hardware and Software Innovations for Energy Efficiency and Process Control Improvement: A Steel Industry Application
by Silvia Maria Zanoli, Crescenzo Pepe and Lorenzo Orlietti
Energies 2023, 16(10), 4183; https://doi.org/10.3390/en16104183 - 18 May 2023
Cited by 1 | Viewed by 1040
Abstract
The present paper proposes a steel industry case study focused on a reheating furnace and a rolling mill. Hardware and software innovations were successfully combined in order to obtain process control and energy efficiency improvement. The reheating furnace at study is pusher type [...] Read more.
The present paper proposes a steel industry case study focused on a reheating furnace and a rolling mill. Hardware and software innovations were successfully combined in order to obtain process control and energy efficiency improvement. The reheating furnace at study is pusher type and processes billets. The hardware innovation is related to the installation of an insulated tunnel at the end of the reheating furnace, in order to guarantee a higher heat retention of the billets before their path along the rolling mill stands. The software innovation refers to the design and the installation of an Advanced Process Control system which manipulates the gas flow rate and the stoichiometric ratio of the furnace zones in order to satisfy the control specifications on billets and furnace variables. The control system is based on Model Predictive Control strategy and on a virtual sensor which tracks and estimates the billet features inside/outside the furnace. The designed controller was commissioned on the real plant, providing significant performances in terms of service factor, process control, and energy efficiency. Full article
(This article belongs to the Special Issue Energy Efficiency Improvement in Process Industries)
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15 pages, 4541 KiB  
Article
An Energy Efficient Advanced Comminution Process to Treat Low-Grade Ferrochrome Slag Using High-Pressure Grinding Rolls
by Talasetti Santosh, Chinthapudi Eswaraiah, Shivakumar Irappa Angadi, Sunil Kumar Tripathy, Rahul Kumar Soni and Danda Srinivas Rao
Energies 2023, 16(7), 3139; https://doi.org/10.3390/en16073139 - 30 Mar 2023
Viewed by 1073
Abstract
The present research aims to analyze the comminution behavior of ferrochrome slag using high-pressure grinding rolls. The laboratory bench scale high-pressure grinding rolls were used to study the three significant variables on the grinding efficiency of ferrochrome slag. The Central Composite Design was [...] Read more.
The present research aims to analyze the comminution behavior of ferrochrome slag using high-pressure grinding rolls. The laboratory bench scale high-pressure grinding rolls were used to study the three significant variables on the grinding efficiency of ferrochrome slag. The Central Composite Design was used to study the process variables, such as roll gap, applied load, and roller speed. The grinding efficiency was evaluated based on the product size and the energy consumption. The results showed that the increased gap between the rolls and roller speed decreases the product size with increased energy consumption. The results also found that an increase in applied load decreases the product fineness with increased energy consumption. The models were developed for the responses of P80 (size of 80% mass finer) and Ecs (specific energy consumption). Both the responses show high regression coefficients, thus ensuring adequate models with the experimental data. The minimum values of the P80 size and specific energy were determined using quadratic programming. The optimum values of the roll gap applied load and roll speed were found to be 1.43 mm, 16 kN, and 800 Rpm, respectively. The minimum values of P80 and the specific energy consumption were found to be 1264 µm and 0.56 kWh/t, respectively. Full article
(This article belongs to the Special Issue Energy Efficiency Improvement in Process Industries)
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