Special Issue "Energy, Economic and Environment for Industrial Production Processes"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Other Topics".

Deadline for manuscript submissions: 31 October 2019

Special Issue Editors

Guest Editor
Dr. Wei Cai

Department of Mechanical Engineering, Southwest University, Beibei District, Chongqing 400715, China
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Interests: green manufacturing; sustainability; energy efficient production; energy management; energy saving
Guest Editor
Prof. Dr. Guangdong Tian

School of Mechanical Engineering, Shandong University, 27 Shanda Nanlu, Jinan 250100, China
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Interests: remanufacturing and green manufacturing; green logistics and transportation; intelligent inspection and repair of automotive
Guest Editor
Prof. MengChu Zhou

Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
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Interests: Petri nets; intelligent automation; Internet of Things; big data; web services; and intelligent
Guest Editor
Prof. Fu Zhao

School of Mechanical Engineering, Purdue University, 585 Purdue Mall West Lafayette, IN 47907-2088, USA
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Interests: life cycle assessment; design for the environment; sustainable manufacturing; renewable energy and energy policy
Guest Editor
Dr. Jorge Cunha

ALGORITMI Research Center, University of Minho, Portugal
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Interests: sustainability; renewable energy technologies; innovation; energy economics and environmental economics transportation

Special Issue Information

Dear Colleagues,

Facing significant natural resource consumption, environmental degradation, and the resulting climate warming, national administrations have increased their focus on ecological modernization, green growth, and low carbon development, with a national sustainability strategy. The industrial sector, recognized as an important pillar in the national economy, brings vast amounts of energy consumption at a low efficiency and high carbon emissions, becoming the walls to the coordinated development of the energy, economic, and environment. Studying the nexus between energy, economy, and environment (3E) of industrial production processes is a significant issue for promoting industrial sustainable development.

The scope of the Processes journal covers research in chemistry, biology, materials and allied engineering fields. Thus, in this Special Issue, we invite articles focused on research regarding the chemistry, biology, materials and allied engineering firms (manufacturing processes, iron and steel production processes, mining processes, power generation processes, and so on).

This Special Issue will focus on publishing original research works about 3E for industrial production processes including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. It also welcomes studies that stimulate the research discussion of moving towards production in a particular industrial sector.

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

  • The nexus between 3E in industrial production processes
  • 3E modelling and analysis in industrial production processes
  • 3E assessment and optimization in industrial production processes
  • Performance indicators and certifications of 3E in industrial production processes
  • Data-driven 3E for industrial production processes
  • Green production and process optimization
  • Cleaner energy production
  • Carbon emission and accounting in the industrial sector
  • Industrial energy efficiency

Dr. Wei Cai
Prof. Dr. Guangdong Tian
Prof. MengChu Zhou
Prof. Fu Zhao
Dr. Jorge Cunha
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 papers will be 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. Processes is an international peer-reviewed open access monthly 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 1100 CHF (Swiss Francs). Please note that for papers submitted after 30 June 2019 an APC of 1200 CHF applies. 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, economic and environment
  • Industrial production processes
  • Carbon emission
  • Green production
  • Sustainability

Published Papers (9 papers)

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Research

Open AccessArticle Enhancement Effect of Ordered Hierarchical Pore Configuration on SO2 Adsorption and Desorption Process
Processes 2019, 7(3), 173; https://doi.org/10.3390/pr7030173 (registering DOI)
Received: 28 February 2019 / Revised: 18 March 2019 / Accepted: 19 March 2019 / Published: 25 March 2019
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Abstract
Carbonaceous adsorbents with both high sulfur capacity and easy regeneration are required for flue gas desulfurization. A hierarchical structure is desirable for SO2 removal, since the micropores are beneficial for SO2 adsorption, while the mesopore networks facilitate gas diffusion and end-product [...] Read more.
Carbonaceous adsorbents with both high sulfur capacity and easy regeneration are required for flue gas desulfurization. A hierarchical structure is desirable for SO2 removal, since the micropores are beneficial for SO2 adsorption, while the mesopore networks facilitate gas diffusion and end-product H2SO4 storage. Herein, an ordered hierarchical porous carbon was synthesized via a soft-template method and subsequent activation, used in SO2 removal, and compared with coal-based activated carbon, which also had a hierarchical pore configuration. The more detailed, abundant micropores created in CO2 activation, especially the ultramicropores (d < 0.7 nm), are essential in enhancing the SO2 adsorption and the reserves rather than the pore patterns. While O2 and H2O participate in the reaction, the hierarchical porous carbon with ordered mesopores greatly improves SO2 removal dynamics and sulfur capacity, as this interconnecting pore pattern facilitates H2SO4 transport from micropores to mesopores, releasing the SO2 adsorption space. Additionally, the water-washing regeneration performances of the two types of adsorbents were comparatively determined and provide a new insight into the mass-transfer resistance in the pore structure. The ordered hierarchical carbon promoted H2SO4 desorption efficiency and cycled SO2 adsorption–desorption performance, further indicating that interconnecting micro- and mesopores facilitated the diffusion of adsorbates. Full article
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
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Open AccessArticle Realizing Energy Savings in Integrated Process Planning and Scheduling
Processes 2019, 7(3), 120; https://doi.org/10.3390/pr7030120
Received: 25 January 2019 / Revised: 20 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
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Abstract
The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses [...] Read more.
The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses of energy. In fact, with a deteriorating global climate caused by massive coal-fired power consumption, carbon emission reduction in the manufacturing sector is becoming increasingly imperative. To ease the environmental burden caused by energy consumption, e.g., coal-fired power consumption in use of machine tools, this research considers both makespan as well as environmental performance criteria, e.g., total power consumption, in integrated process planning and scheduling using a novel multi-objective memetic algorithm to facilitate a potential amount of energy savings; this can be realized through a better use of resources with more efficient scheduling schemes. A mixed-integer linear programming (MILP) model based on the network graph is formulated with both makespan as well as total power consumption criteria. Due to the complexity of the problem, a multi-objective memetic algorithm with variable neighborhood search (VNS) technique is then developed for this problem. The Kim’s benchmark instances are employed to test the proposed algorithm. Moreover, the TOPSIS decision method is used to determine the most satisfactory non-dominated solution. Several scenarios are considered to simulate different machine automation levels and different machine workload levels. Computational results show that the proposed algorithm can strike a balance between the makespan criterion and the total power consumption criterion, and the total power consumption can be affected by machine tools with different automation levels and different workloads. More importantly, results also show that energy saving can be realized by completing machining as early as possible on a machine tool and taking advantage of machine flexibility. Full article
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
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Open AccessArticle A Composite Evaluation Model of Sustainable Manufacturing in Machining Process for Typical Machine Tools
Processes 2019, 7(2), 110; https://doi.org/10.3390/pr7020110
Received: 30 December 2018 / Revised: 15 February 2019 / Accepted: 15 February 2019 / Published: 20 February 2019
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Abstract
Machine tool is the basic manufacturing equipment in today’s mechanical manufacturing industry. A considerable amount of energy and carbon emission are consumed in machining processes, the realization of sustainable manufacturing of machine tools have become an urgent problem to be solved in the [...] Read more.
Machine tool is the basic manufacturing equipment in today’s mechanical manufacturing industry. A considerable amount of energy and carbon emission are consumed in machining processes, the realization of sustainable manufacturing of machine tools have become an urgent problem to be solved in the field of industry and academia. Therefore, five types of machine tools were selected for the typical machining processes (turning, milling, planning, grinding and drilling). Then the model of the energy efficiency, carbon efficiency and green degree model were established in this paper which considers the theory and experiment with the resource, energy and emission modeling method. The head frame spindle and head frame box were selected to verify the feasibility and practicability of the proposed model, based on the orthogonal experiment case of the key machining process. In addition, the influence rules of machining parameters were explored and the energy efficiency and green degree of the machine tools were compared. Finally, the corresponding strategies for energy conservation and emission reduction were proposed. Full article
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
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Open AccessArticle Using the Optimization Algorithm to Evaluate the Energetic Industry: A Case Study in Thailand
Processes 2019, 7(2), 87; https://doi.org/10.3390/pr7020087
Received: 17 December 2018 / Revised: 3 February 2019 / Accepted: 5 February 2019 / Published: 9 February 2019
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Abstract
Thailand’s economy is developing rapidly, with energy being a significant factor in this development. This study uses a variety of models to assess the performance of Thailand’s energy industry in two different phases, the first being from 2013 to 2017 and the second [...] Read more.
Thailand’s economy is developing rapidly, with energy being a significant factor in this development. This study uses a variety of models to assess the performance of Thailand’s energy industry in two different phases, the first being from 2013 to 2017 and the second from 2018 to 2020. The Malmquist model-one of data envelopment required input and output data that showed Thailand’s productivity index and the rate-of-change ratio, which is used to assess technical changes, change efficiency, and productivity changes of the 12 listed companies in energetic generation and distribution in Thailand. To calculate future indicators, the forecast data are generated by applying the Grey model (1,1) GM(1,1). Accuracy prediction is determined by the mean absolute percentage error (MAPE). The results show that the magnitude of the change in efficiency during the first period is stable, and some major changes in the technical level of some companies may be observed. In the future, the performance of most companies has increased steadily, but performance has been outstanding. This research provides insights into Thailand’s energy over the past few years, and predictions of future performance may be used as a reference for more purposes. Full article
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
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Open AccessArticle Formation Mechanism of Trailing Oil in Product Oil Pipeline
Processes 2019, 7(1), 7; https://doi.org/10.3390/pr7010007
Received: 7 November 2018 / Revised: 15 December 2018 / Accepted: 17 December 2018 / Published: 25 December 2018
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Abstract
Trailing oil is the tail section of contamination in oil pipelines. It is generated in batch transportation, for which one fluid, such as diesel oil follows another fluid, such as gasoline, and it has an effect on the quality of oil. This paper [...] Read more.
Trailing oil is the tail section of contamination in oil pipelines. It is generated in batch transportation, for which one fluid, such as diesel oil follows another fluid, such as gasoline, and it has an effect on the quality of oil. This paper describes our analysis of the formation mechanism of trailing oil in pipelines and our study of the influence of dead-legs on the formation of trailing oil. We found that the oil replacement rate in a dead-leg is exponentially related to the flow speed, and the length of the dead-leg is exponentially related to the replacement time of the oil. To reduce the amount of mixed oil, the main flow speed should be kept at about 1.6 m/s, and the length of the dead-leg should be less than five times the diameter of the main pipe. In our work, the Reynolds time-averaged method is used to simulate turbulence. To obtain contamination-related experimental data, computational fluid dynamics (CFD) software is used to simulate different flow rates and bypass lengths. MATLAB software was used to perform multi-nonlinear regression for the oil substitution time, the length of the bypass, and the flow speed. We determined an equation for calculating the length of the trailing oil contamination produced by the dead-leg. A modified equation for calculating the length of the contamination was obtained by combining the existing equation for calculating the length of the contamination with new factors based on our work. The amounts of contamination predicted by the new equation is closer to the actual contamination amounts than predicted values from other methods suggested by previous scholars. Full article
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
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Open AccessArticle Measuring Efficiency of Generating Electric Processes
Processes 2019, 7(1), 6; https://doi.org/10.3390/pr7010006
Received: 12 November 2018 / Revised: 8 December 2018 / Accepted: 19 December 2018 / Published: 21 December 2018
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Abstract
Electric energy sources are the foundation for supporting for the industrialization and modernization; however, the processes of electricity generation increase CO2 emissions. This study integrates the Holt–Winters model in number cruncher statistical system (NCSS) to estimate the forecasting data and the undesirable [...] Read more.
Electric energy sources are the foundation for supporting for the industrialization and modernization; however, the processes of electricity generation increase CO2 emissions. This study integrates the Holt–Winters model in number cruncher statistical system (NCSS) to estimate the forecasting data and the undesirable model in data envelopment analysis (DEA) to calculate the efficiency of electricity production in 14 countries all over the world from past to future. The Holt–Winters model is utilized to estimate the future; then, the actual and forecasting data are applied into the undesirable model to compute the performance. From the principle of an undesirable model, the study determines the input and output factors as follows nonrenewable and renewable fuels (inputs), electricity generation (desirable output), and CO2 emissions (undesirable output). The empirical results exhibit efficient/inefficient terms over the period from 2011–2021 while converting these fuels into electricity energy and CO2 emissions. The efficiency reveals the environmental effect level from the electricity generation. The analysis scores recommend a direction for improving the inefficient terms via the principle of inputs and undesirable outputs excess and desirable outputs shortfalls in an undesirable model. Full article
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
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Open AccessArticle Receding Horizon Optimization of Wind Farm Active Power Distribution with Power Tracking Error and Transmission Loss
Processes 2018, 6(12), 259; https://doi.org/10.3390/pr6120259
Received: 13 November 2018 / Revised: 27 November 2018 / Accepted: 5 December 2018 / Published: 10 December 2018
Cited by 1 | PDF Full-text (858 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a receding-horizon optimization strategy is introduced to optimize the wind farm active power distribution with power tracking error and transmission loss. Based on the wind farm transmission connections, a wind farm can be divided into clusters, in which the wind [...] Read more.
In this paper, a receding-horizon optimization strategy is introduced to optimize the wind farm active power distribution with power tracking error and transmission loss. Based on the wind farm transmission connections, a wind farm can be divided into clusters, in which the wind turbine generator systems connected to one booster station can be taken as one cluster, and different clusters connected from booster stations to the farm-level main transformer output the electric power to the grid. Thus, in the proposed strategy, the power tracking characteristic of the wind turbine generator system is modeled as a first-order system to quantify the power tracking error during the power tracking dynamic process, where the power transmission losses from wind turbine generator systems to booster stations are also modeled and considered in the optimization. The proposed strategy is applied to distribute the active power set-point within a cluster while the clusters’ set-point still follows the conventional strategy of the wind farm. Simulation results show significant improvement for both optimization targets of the proposed strategy. Full article
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
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Open AccessArticle Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm
Processes 2018, 6(12), 250; https://doi.org/10.3390/pr6120250
Received: 7 November 2018 / Revised: 30 November 2018 / Accepted: 30 November 2018 / Published: 4 December 2018
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Abstract
To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the [...] Read more.
To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers’ different, even conflicting, preferences can be automatically determined from the set of Pareto-optimal solutions. The primary contribution of the proposal is the innovative application of many-objective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems. Full article
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
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Open AccessArticle Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy
Processes 2018, 6(11), 225; https://doi.org/10.3390/pr6110225
Received: 23 October 2018 / Revised: 6 November 2018 / Accepted: 13 November 2018 / Published: 16 November 2018
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Abstract
In this paper we propose a methodology to study circular economy processes based on mathematical modelling. In open-ended systems, waste could be converted back to recycling, transforming the economy from linear to circular. The concept of entropy and the second law of thermodynamics [...] Read more.
In this paper we propose a methodology to study circular economy processes based on mathematical modelling. In open-ended systems, waste could be converted back to recycling, transforming the economy from linear to circular. The concept of entropy and the second law of thermodynamics give the argument for a scale reduction of material circulation. As humans extract more and more energy and matter for the economy, the degree of entropy is likely to increase. Based on the findings of economic studies on the implications of industrialization in the case of growing economies, this study aims at evaluating circular economy processes at the European Union (EU) level using a Shannon-Entropy-based algorithm. An entropy-based analysis was conducted for the 28 European Union countries during the time frame 2007–2016. The modelling process consists of constructing a composite indicator which is composed of a weighted sum of all indicators developed by an algorithm based on Shannon Entropy. The weights assigned to each indicator in our analysis measure the significance of each indicator involved in the development of the composite indicator. The results are similar to the international rakings, consolidating and confirming the accuracy and reliability of this approach. Full article
(This article belongs to the Special Issue Energy, Economic and Environment for Industrial Production Processes)
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