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Data Driven Approaches for Environmental Sustainability 2023

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 24129

Special Issue Editor


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Guest Editor
1. Center for Quantitative Genetics and Genomics, Aarhus University, 8000 Aarhus, Denmark
2. Renewable and Sustainable Energy Research Center, Technology Innovation Institute, Abu Dhabi, United Arab Emirates
Interests: time series analysis; software package development; prediction applications in natural entities such as wind energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The “three pillars of sustainability” concept, which encompasses social, economic, and environmental sustainability, is well-known and very important in society. However, currently, greater importance is given to social and economic development at the cost of environmental sustainability. In recent decades, major topic discussed in this area have included the harvesting of renewable resources, the depletion of non-renewable resources, and the creation of pollution. Recent advancements in data analysis techniques and methodologies along with higher-level computational infrastructures have presented many new dimensions to observe patterns and provide more effective solutions. For this Special Issue of Energies on “Data-driven approaches for environmental sustainability”, we invite authors to submit articles on, but not limited to, the following topics: data-analysis-driven analysis, policies, and case studies of environmental parameters such as renewable energy, air and water pollution, and water leakage management; enhancement of data analysis techniques such as predictions, time series forecasting, data imputations, optimization methodologies, and their applications in environmental sustainability; data collection, data cleaning, and novel visualization techniques; data analysis tools, software, and packages for use in environmental sustainability.

Dr. Neeraj Bokde
Guest Editor

Manuscript Submission Information

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Keywords

  • environment
  • energy pollution
  • wind solar
  • renewable energy generation
  • time series analysis
  • data mining
  • water resource management
  • environment sustainability

Published Papers (13 papers)

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Research

21 pages, 1359 KiB  
Article
Temporal Analysis of Energy Transformation in EU Countries
by Paweł Ziemba and Abdullah Zair
Energies 2023, 16(23), 7703; https://doi.org/10.3390/en16237703 - 22 Nov 2023
Cited by 2 | Viewed by 695
Abstract
Due to the environmental policy adopted by the European Union (EU), EU countries are obliged to reduce greenhouse gas emissions. They reduce emissions largely through the energy transformation and switching to renewable energy sources (RES). Therefore, it is important to assess the progress [...] Read more.
Due to the environmental policy adopted by the European Union (EU), EU countries are obliged to reduce greenhouse gas emissions. They reduce emissions largely through the energy transformation and switching to renewable energy sources (RES). Therefore, it is important to assess the progress of the energy transformation of individual EU countries. This is related to the aim of the article, which is a temporal analysis of the energy transformation process towards the transition to RES and reducing the use of fossil fuels in energy production. To achieve this goal, a new Temporal/Dynamic Multi-Criteria Decision-Making (T/DMCDM) method called Temporal PROSA was developed, based on the PROMETHEE and PROSA family of methods. The Temporal PROSA method, unlike many other T/DMCDM methods, enables the aggregation of data from many periods into a single final assessment, as well as the direct transfer of information from the examined periods to the overall result. As a result of the research, EU countries that dominated in terms of progress in energy transformation towards RES in the years 2004–2021were identified. Based on the data and methodology used, it was indicated that these countries are primarily Sweden and Portugal, and recently also Denmark and Finland. On the other hand, countries such as Belgium, Bulgaria, Cyprus, Luxembourg, and Poland made the least progress between 2004 and 2021. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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20 pages, 1296 KiB  
Article
Technology Route Options of China’s Sustainable Aviation Fuel: Analysis Based on the TOPSIS Method
by Yuxiu Chen, Chunxia Xu, Xiaojun Yang, Xiaojia He, Zongwei Zhang, Jian Yu, Liyan Quan and Shiqi Yang
Energies 2023, 16(22), 7597; https://doi.org/10.3390/en16227597 - 15 Nov 2023
Viewed by 1514
Abstract
Developing production technology pathways of sustainable aviation fuel (SAF) that align with China’s national conditions and aviation transportation needs is crucial for promoting the SAF industry and achieving China’s carbon peak and carbon neutrality goals. This article first projects the future SAF demand [...] Read more.
Developing production technology pathways of sustainable aviation fuel (SAF) that align with China’s national conditions and aviation transportation needs is crucial for promoting the SAF industry and achieving China’s carbon peak and carbon neutrality goals. This article first projects the future SAF demand in China for the coming decades. Using SAF demand data as an input, this article employs the TOPSIS analysis method to comprehensively evaluate the suitability of four SAF production technology pathways at different stages of development in China, which are Hydroprocessed Esters and Fatty Acids (HEFA), Alcohol-to-Jet (AtJ), Natural Gas + Fischer–Tropsch Synthesis (G + FT), and Power-to-Liquid (PtL). The research results reveal the following trends: HEFA-based processes are the most suitable technology pathways for China in the near term; the G + FT route, based on energy crops, appears the most likely to support civil aviation needs in the medium to long term. In the long run, the PtL route holds significant potential, especially with the decreasing costs of green electricity, advancements in carbon capture, utilization, and storage (CCUS) technology, and improvements in SAF synthesis methods. In the final section of this article, we provide recommendations to drive the development of the SAF industry in China. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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28 pages, 1598 KiB  
Article
Developing a Comprehensive Mathematical Model for Aluminium Production in a Soderberg Electrolyser
by Yury Valeryevich Ilyushin and Ekaterina Ivanovna Kapostey
Energies 2023, 16(17), 6313; https://doi.org/10.3390/en16176313 - 30 Aug 2023
Cited by 32 | Viewed by 1529
Abstract
The technological process of aluminium electrolysis is a complex scientific and technical task. This is due to a large number of internal, external and resultant factors. The aim of this work is to analyse these factors, assess them and their influence on the [...] Read more.
The technological process of aluminium electrolysis is a complex scientific and technical task. This is due to a large number of internal, external and resultant factors. The aim of this work is to analyse these factors, assess them and their influence on the technological process of electrolysis and develop a comprehensive and mathematical model of aluminium production in the Soderberg electrolyser. The work analyses the technological process of primary aluminium production on the basis of the Bayer method and then on the basis of the Hall–Eru method. The existing methods and technologies for computer modelling of the technological process are analysed. The modern methods of analysis for thermal and electromagnetic fields in electrolysers are considered. On the basis of an in-depth analysis, a number of factors influencing the process of primary aluminium production are identified. Using the methods of system analysis to analyse the identified factors, a ranked list of factors according to the degree of influence is obtained. Using the Pareto diagram, we obtain a list of factors with maximum impact. A conceptual model of the technological process is derived. Based on the obtained conceptual model, the mathematical model of the technological process is derived. The conducted research may be useful to specialists in the field of metallurgy for the analysis of the technological processes of primary aluminium production. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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14 pages, 2903 KiB  
Article
Performance Evaluation of Irrigation Canals Using Data Envelopment Analysis for Efficient and Sustainable Irrigation Management in Jharkhand State, India
by Jay Nigam, Totakura Bangar Raju and Ramachandra K. Pavan Kumar Pannala
Energies 2023, 16(14), 5490; https://doi.org/10.3390/en16145490 - 20 Jul 2023
Cited by 1 | Viewed by 1164
Abstract
Across the world, achieving sustainable water resource development and managing limited natural resources like water have proven to be significant issues, and India is no exception to the same. At present, irrigation consumes about 80 percent of the total available water in India [...] Read more.
Across the world, achieving sustainable water resource development and managing limited natural resources like water have proven to be significant issues, and India is no exception to the same. At present, irrigation consumes about 80 percent of the total available water in India and is predicted to remain the dominant water user. India is already a country with water stress and is on its way to becoming a country with water scarcity. Rising agricultural productivity, expanding urbanisation, growing industrialisation, and accelerating industry create competing demands for water. Most irrigation system issues in India are attributable to poor management of the built infrastructure. The present study aims to rank the irrigation canals based on their efficiencies and inefficiencies to improve the overall performance of WUAs in Jharkhand. The ranking is done for nine irrigation canals based on financial inputs like investments in training and development of WUAs, maintenance and repairs of canals, and outputs like users’ charges collection and yield in crop production. The Comprehensive Efficiency score analysis suggests that while there is room for improvement in the performance, the Water Users’ Associations have generally been successful in improving the technical efficiency of the irrigation system. The findings can be used for further investigation to identify best practises and areas for improvement. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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26 pages, 2708 KiB  
Article
Analysis of the Level of Efficiency of Control Methods in the Context of Energy Intensity
by Andrzej Pacana, Karolina Czerwińska and Grzegorz Ostasz
Energies 2023, 16(8), 3507; https://doi.org/10.3390/en16083507 - 18 Apr 2023
Cited by 1 | Viewed by 990
Abstract
In enterprises, the management of detection methods usually refers to ensuring the identification of nonconformities. This management is incomplete and incompatible with the concept of sustainability (it ignores electricity consumption and costs). To date, no models have been developed to support the analysis [...] Read more.
In enterprises, the management of detection methods usually refers to ensuring the identification of nonconformities. This management is incomplete and incompatible with the concept of sustainability (it ignores electricity consumption and costs). To date, no models have been developed to support the analysis of detection methods in terms of the relationship of efficiency–energy consumption. The purpose of the study was to develop proprietary software to analyse the level of efficiency of detection methods for casting products in the context of their energy intensity. The model supports effective management of the quality control process, optimising the relationship of product quality–energy intensity of the process. The model integrally combines detection methods, so it was possible to identify critical product nonconformities and analyse these methods to determine their effectiveness, time efficiency, cost efficiency, and energy intensity. As a result of the implications of the software, a ranking of the total efficiency of electrical connector detection methods was obtained. The numerical values of the total efficiency index indicated that X-ray testing was the most effective, well ahead of the other methods. The eddy current and ultrasonic tests show similar values for the index analysed. A verification of the software was carried out that confirmed its suitability in foundry enterprises. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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10 pages, 5087 KiB  
Article
Data-Driven Internal Carbon Pricing Mechanism for Improving Wood Procurement in Integrated Energy and Material Production
by Teijo Palander
Energies 2023, 16(8), 3473; https://doi.org/10.3390/en16083473 - 15 Apr 2023
Cited by 2 | Viewed by 1203
Abstract
More than 25% of the total energy consumption in Finland has been produced with wood fuels. Since 2012, the share has been greater than that of oil, coal, or natural gas. Internal carbon pricing is used to manage the risks in wood procurement [...] Read more.
More than 25% of the total energy consumption in Finland has been produced with wood fuels. Since 2012, the share has been greater than that of oil, coal, or natural gas. Internal carbon pricing is used to manage the risks in wood procurement after wood import from Russia ended. Further, the EU announced plans to sell more carbon emission permits to fund the EU’s exit from Russian energy. To manage these challenges, a data-driven internal carbon pricing mechanism (DDICPM) has been developed for wood procurement optimization. Particularly, local changes are considered via available information about growth-based carbon sinks (GBCS). The results of the new scenario were compared to the basic national scenario that ensures carbon neutrality in forestry. The DDICPM may provide the optimum wood-procurement operations maintaining carbon neutrality in the integrated energy and material industry (IEMI). In this study, the use of DDICPM increased profitability b 16.2, 16.1, and 16.0% between adapted wood procurement areas at the EU’s emission allowance prices of 30, 65, and 98 € t−1 CO2. The experiments’ results also revealed that the DDICPM could consistently and significantly outperform the conventional solution adopted by the company in terms of economic costs. A significant conclusion is that an increase in profitability is possible if the size of wood procurement areas is allowed to vary optimally with respect to transport distance to take advantage of the GBCS as a new application of the renewable carbon sink. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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18 pages, 2861 KiB  
Article
From Secondary Biomass to Bio-Methanol through CONVERGE Technology: An Environmental Analysis
by Stefan Cristian Galusnyak, Letitia Petrescu, Dora Andreea Chisalita, Calin-Cristian Cormos and Marco Ugolini
Energies 2023, 16(6), 2726; https://doi.org/10.3390/en16062726 - 15 Mar 2023
Cited by 2 | Viewed by 2021
Abstract
Owing to residual biomass availability, the share of advanced biofuels produced from secondary biomass is forecasted to increase and significantly contribute towards achieving net-zero emissions. The current work investigates bio-methanol production through a new process configuration designed to improve the environmental performance when [...] Read more.
Owing to residual biomass availability, the share of advanced biofuels produced from secondary biomass is forecasted to increase and significantly contribute towards achieving net-zero emissions. The current work investigates bio-methanol production through a new process configuration designed to improve the environmental performance when compared to the state-of-the art technologies (Base Case). The environmental evaluation is conducted according to the Life Cycle Assessment (LCA) methodology. ReCiPe was employed as an impact assessment method with the aid of GaBi software. Depending on the plant geographical location, wooden biomass and exhausted olive pomace were evaluated as biomass sources. A scenario analysis targeting different energy sources was performed as well. The outcome of the environmental evaluation highlights a better performance in eight of a total of nine impact categories studied in the wooden biomass scenarios compared to the exhausted olive pomace. Moreover, two of the CONVERGE technology cases were compared against the Base Case. As the results show, CONVERGE technology registers a lower score in at least six of the impact categories studied. Concerning the total CO2 emissions, CONVERGE exhibits a better performance compared to the Base Case, if the additional amount of CO2 is either stored, sold as a by-product or vented into the atmosphere. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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21 pages, 1642 KiB  
Article
A New QFD-CE Method for Considering the Concept of Sustainable Development and Circular Economy
by Dominika Siwiec, Andrzej Pacana and Andrzej Gazda
Energies 2023, 16(5), 2474; https://doi.org/10.3390/en16052474 - 5 Mar 2023
Cited by 6 | Viewed by 2553
Abstract
The idea of sustainable development (SD) forces companies to combine the quality development of products with the simultaneous care of the natural environment. These actions should start with the product design process. The aim of the study was to create a modified method [...] Read more.
The idea of sustainable development (SD) forces companies to combine the quality development of products with the simultaneous care of the natural environment. These actions should start with the product design process. The aim of the study was to create a modified method of Quality Function Development (QFD-CE), which will support the design of new products or improve the existing products on the market. In the proposed method (QFD-CE), the method integrates techniques such as: SMARTER method, brainstorming (BM), the method of selecting a team of experts, kinship diagram, fixed sum scale, and Likert scale. A novelty compared to the traditional QFD methodology is that design goals are set not only based on customer expectations, but also considering the impact on the natural environment. The originality of this proposition comes to the practical inclusion of including sustainability development criteria. The proposed method can be used in companies that design new products and are focused on caring for the natural environment. The QFD-CE method test method was performed for photovoltaic panels (PV). As part of the proposed QFD-CE method, the sequence of design activities was determined so that they meet customer expectations and can be simultaneously implemented according to the idea of SD. This method can be used for any product, mainly those that have a significant impact on the natural environment. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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24 pages, 4197 KiB  
Article
Impacts of Tariffs on Energy Conscious Behavior with Respect to Household Attributes in Saudi Arabia
by Kh Md Nahiduzzaman, Abdullatif Said Abdallah, Arash Moradzadeh, Amin Mohammadpour Shotorbani, Kasun Hewage and Rehan Sadiq
Energies 2023, 16(3), 1458; https://doi.org/10.3390/en16031458 - 1 Feb 2023
Cited by 2 | Viewed by 1312
Abstract
Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential [...] Read more.
Historically, the combination of generous subsidies along with extreme climate has led to unsustainable domestic electricity consumption in Saudi Arabia. The residential sector constitutes a significant portion of this consumption. Amid the economic challenges, the country enforced a new electricity tariff for residential consumers in 2018. This study thus leverages change in 2018–2020 by collecting and analyzing the electricity consumption data of 73 households in the Eastern Province of Saudi Arabia. The energy consumption is modeled based on the households’ attributes (e.g., dwelling type, ownership, number of residents, rooms, ventilation type, etc.) and applied tariffs using a machine learning technique. The extreme learning machine (ELM) is employed in solving the overfitting problem due to low-volume data. The correlation matrix is also constructed to determine the relationship between the household attributes. The ELM model developed in this study extracts the correlation between the input variables in determining energy consumption and also predicts the energy consumption related to low consumption data. The findings indicated that the electricity consumption between the pre-revised tariff year and the revised tariff enforcement year saw a reduction which was consistent in the subsequent years. This was also validated by the paired sample t-test, which showed a significant decrease in electricity consumption for the study period. The analysis also revealed that several household attributes had a relatively high impact on the reduction in the electricity consumption level following the revised tariffs, whereas the majority of the attributes had a moderate impact. In addition to these key findings, the demonstrated pathway adopted in this study is itself a methodological contribution that provides critical information about the sensitivity of the impacts of tariffs on energy consumption with respect to different household attributes. Economic factors being the critical stress need to be blended with existing energy consciousness for positive changes in favor of energy-saving behavior of the household members. The study does not attempt to represent the population of concern, but demonstrates a methodology that would help unleash inherent energy consciousness in favor of sustainable and energy-efficient behavior. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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21 pages, 3764 KiB  
Article
A Comparative Analysis of Hyperparameter Tuned Stochastic Short Term Load Forecasting for Power System Operator
by B. V. Surya Vardhan, Mohan Khedkar, Ishan Srivastava, Prajwal Thakre and Neeraj Dhanraj Bokde
Energies 2023, 16(3), 1243; https://doi.org/10.3390/en16031243 - 23 Jan 2023
Cited by 7 | Viewed by 1794
Abstract
Intermittency in the grid creates operational issues for power system operators (PSO). One such intermittent parameter is load. Accurate prediction of the load is the key to proper planning of the power system. This paper uses regression analyses for short-term load forecasting (STLF). [...] Read more.
Intermittency in the grid creates operational issues for power system operators (PSO). One such intermittent parameter is load. Accurate prediction of the load is the key to proper planning of the power system. This paper uses regression analyses for short-term load forecasting (STLF). Assumed load data are first analyzed and outliers are identified and treated. The cleaned data are fed to regression methods involving Linear Regression, Decision Trees (DT), Support Vector Machine (SVM), Ensemble, Gaussian Process Regression (GPR), and Neural Networks. The best method is identified based on statistical analyses using parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), R2, and Prediction Speed. The best method is further optimized with the objective of reducing MSE by tuning hyperparameters using Bayesian Optimization, Grid Search, and Random Search. The algorithms are implemented in Python and Matlab Platforms. It is observed that the best methods obtained for regression analysis and hyperparameter tuning for an assumed data set are Decision Trees and Grid Search, respectively. It is also observed that, due to hyperparameter tuning, the MSE is reduced by 12.98%. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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16 pages, 579 KiB  
Article
Bioethanol Production from Lignocellulosic Biomass Using Aspergillus niger and Aspergillus flavus Hydrolysis Enzymes through Immobilized S. cerevisiae
by Amira H. Alabdalall, Asma A. Almutari, Sumayh A. Aldakeel, Ahmed M. Albarrag, Lena A. Aldakheel, Maryam H. Alsoufi, Lulwah Y. Alfuraih and Hesham M. Elkomy
Energies 2023, 16(2), 823; https://doi.org/10.3390/en16020823 - 11 Jan 2023
Cited by 2 | Viewed by 3345
Abstract
Lignocellulose, the main component of a plant cell wall, is a potential renewable bioenergy source. It is composed of cellulose, hemicellulose, and lignin structures. Cellulose is a linear polysaccharide that is hydrolyzed chemically or enzymatically by cellulase. The addition of lignocellulosic biomass, such [...] Read more.
Lignocellulose, the main component of a plant cell wall, is a potential renewable bioenergy source. It is composed of cellulose, hemicellulose, and lignin structures. Cellulose is a linear polysaccharide that is hydrolyzed chemically or enzymatically by cellulase. The addition of lignocellulosic biomass, such as wheat bran and coffee pulp, into the fermentation culture, induces the production of cellulases. Cellulose accounts for 20% of the enzyme market worldwide, demonstrating benefits in diverse applications, especially bioethanol and biogas generation. The aim is to evaluate the optimal condition for bioethanol production by previously isolated fungal species from different soil types in the eastern region of the Kingdom of Saudi Arabia. This study attempts to evaluate and optimize the culture conditions of lignocellulosic biomass under SSF using the highest cellulases-producer strains in the region: Aspergillus niger and Aspergillus flavus (GenBank Accession No. MT328516 and MT328429, respectively) to produce raw sugar that consequently is used in the next step of bioethanol production. This process has two parts: (1) hydrolyze lignocellulosic biomass to obtain raw sugar using A. niger and A. flavus that produce cellulase, and (2) produce bioethanol through the conversion of the raw sugar produced from the cellulolysis into ethanol using Saccharomyces cerevisiae. The optimal conditions under SSF were seven days of incubation, 5% glucose as a carbon source, 1% ammonium sulfate as a nitrogen source, and 80% moisture for both isolates. Biochemical characterization showed stability for the immobilized enzyme in all temperature ranges (from 20 °C to 70 °C), while the free enzyme exhibited its maximum at 20 °C of 1.14 IU/mL. CMCase production was the highest at pH 4.0 (1.26 IU/mL) for free enzyme and at pH 5.0 (2.09 IU/mL) for the immobilized form. The CMCase activity increased steadily with an increase in water level and attained a maximum of 80% moisture content. The maximum enzyme activity was with coffee pulp as a substrate of 7.37 IU/mL and 6.38 IU/mL for A. niger and A. flavus after seven days of incubation, respectively. The Carboxymethyl Cellulase (CMCase) activity in immobilized enzymes showed good storage stability under SSF for six weeks, maintaining 90% of its initial activity, while the free enzyme retained only 59% of its original activity. As a carbon source, glucose was the best inducer of CMCase activity with coffee pulp substrate (7.41 IU/mL and 6.33 IU/mL for A. niger and A. flavus, respectively). In both fungal strains, ammonium sulfate caused maximum CMCase activities with coffee pulp as substrate (7.62 IU/mL and 6.47 IU/mL for A. niger and A. flavus, respectively). Immobilized S. cerevisiae showed an increase in ethanol production compared to free cells. In the case of immobilized S. cerevisiae cells, the concentration of ethanol was increased steadily with increasing fermentation time and attained a maximum of 71.39 mg/mL (A. niger) and 11.73 mg/mL (A. flavus) after 72 h of fermentation. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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13 pages, 3437 KiB  
Article
Machine Learning Assisted Chemical Process Parameter Mapping on Lignin Hydrogenolysis
by Yin Liu, Shuo Cheng and Jeffrey Scott Cross
Energies 2023, 16(1), 256; https://doi.org/10.3390/en16010256 - 26 Dec 2022
Cited by 2 | Viewed by 1325
Abstract
Lignin depolymerization has been studied for decades to produce carbon-neutral chemicals/biofuels and biopolymers. Among different chemical reaction pathways, catalytic hydrogenolysis favors reactions under relatively mild conditions, while its yield of bio-oil and high-value aromatic products is relatively high. In this study, the influence [...] Read more.
Lignin depolymerization has been studied for decades to produce carbon-neutral chemicals/biofuels and biopolymers. Among different chemical reaction pathways, catalytic hydrogenolysis favors reactions under relatively mild conditions, while its yield of bio-oil and high-value aromatic products is relatively high. In this study, the influence of reaction parameters on lignin hydrogenolysis are discussed by chemical process parameter mapping and modeled using three different machine learning algorithms based upon literature experimental data. The best R2 scores for solid residue and aromatic yield were 0.92 and 0.88 for xgboost, respectively. The parameter importance was examined, and it was observed that lignin-to-solvent ratio and average pore size have a larger impact on lignin hydrogenolysis results. Finally, the optimal conditions of lignin hydrogenolysis were predicted by chemical process parameter mapping using the best-fit machine learning model, which indicates that further process improvements can potentially generate higher yields in industrial applications. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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23 pages, 684 KiB  
Article
Performance of Parabolic Trough Collector with Different Heat Transfer Fluids and Control Operation
by Surender Kannaiyan and Neeraj Dhanraj Bokde
Energies 2022, 15(20), 7572; https://doi.org/10.3390/en15207572 - 14 Oct 2022
Cited by 4 | Viewed by 1978
Abstract
Electricity generation from solar energy has become very desirable because it is abundantly available and eco-friendly. Mathematical modeling of various components of a Solar Thermal Power plant (STP) is warranted to predict the optimal and efficient operation of the plant. The efficiency and [...] Read more.
Electricity generation from solar energy has become very desirable because it is abundantly available and eco-friendly. Mathematical modeling of various components of a Solar Thermal Power plant (STP) is warranted to predict the optimal and efficient operation of the plant. The efficiency and reliability of STPs are maximized based on different operating strategies. Opting for proper Heat Transfer Fluid (HTF), which is proposed in this paper, helps in reducing operating complexity and lowering procurement cost. The Parabolic Trough Collector (PTC) is the heart of STP, where proper focusing of PTC towards solar radiation is the primary task to maximize the outlet temperature of HTF. This maximum temperature plays a major factor due to diurnal solar radiation variation, and its disturbance nature, with the frequent startup and shutdown of STP, is avoided. In this paper, the PTC component is modeled from the first principle, and, with different HTF, the performance of PTC with constant and quadratic solar disturbances is analyzed along with classical control system designs. Through this, the operator will be able to choose proper HTF and resize the plant components depending on plant location and weather conditions. Furthermore, the thermal energy is collected for therminol oil, molten salt, and water; and its performance with different inputs of solar radiation is analyzed along with closed-loop controllers. Thermal energy extracted by therminol oil, molten salt, and water with constant solar radiation results in 81.7%,73.7% and 18.7%, respectively. Full article
(This article belongs to the Special Issue Data Driven Approaches for Environmental Sustainability 2023)
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