Call for Papers in Special Issue “Data Driven Approaches for Environmental Sustainability 2023” at Energies Journal
in Updates
Dear Colleagues,
I am editing a Special Issue “Data Driven Approaches for Environmental Sustainability 2023” at Energies journal. Its information is given below and the detailed flyer for this special issue is available here.
Special Issue Information
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.
Glad to share that following articles are already published in this special issue:
Performance of Parabolic Trough Collector with Different Heat Transfer Fluids and Control Operation
@Article{en15207572, AUTHOR = {Kannaiyan, Surender and Bokde, Neeraj Dhanraj}, TITLE = {Performance of Parabolic Trough Collector with Different Heat Transfer Fluids and Control Operation}, JOURNAL = {Energies}, VOLUME = {15}, YEAR = {2022}, NUMBER = {20}, ARTICLE-NUMBER = {7572}, URL = {https://www.mdpi.com/1996-1073/15/20/7572}, ISSN = {1996-1073}, 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 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.}, DOI = {10.3390/en15207572} }Machine Learning Assisted Chemical Process Parameter Mapping on Lignin Hydrogenolysis
@Article{en16010256, AUTHOR = {Liu, Yin and Cheng, Shuo and Cross, Jeffrey Scott}, TITLE = {Machine Learning Assisted Chemical Process Parameter Mapping on Lignin Hydrogenolysis}, JOURNAL = {Energies}, VOLUME = {16}, YEAR = {2023}, NUMBER = {1}, ARTICLE-NUMBER = {256}, URL = {https://www.mdpi.com/1996-1073/16/1/256}, ISSN = {1996-1073}, 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 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.}, DOI = {10.3390/en16010256} }@Article{en16020823, AUTHOR = {Alabdalall, Amira H. and Almutari, Asma A. and Aldakeel, Sumayh A. and Albarrag, Ahmed M. and Aldakheel, Lena A. and Alsoufi, Maryam H. and Alfuraih, Lulwah Y. and Elkomy, Hesham M.}, TITLE = {Bioethanol Production from Lignocellulosic Biomass Using Aspergillus niger and Aspergillus flavus Hydrolysis Enzymes through Immobilized S. cerevisiae}, JOURNAL = {Energies}, VOLUME = {16}, YEAR = {2023}, NUMBER = {2}, ARTICLE-NUMBER = {823}, URL = {https://www.mdpi.com/1996-1073/16/2/823}, ISSN = {1996-1073}, 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 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.}, DOI = {10.3390/en16020823} }@Article{en16031243, AUTHOR = {Vardhan, B. V. Surya and Khedkar, Mohan and Srivastava, Ishan and Thakre, Prajwal and Bokde, Neeraj Dhanraj}, TITLE = {A Comparative Analysis of Hyperparameter Tuned Stochastic Short Term Load Forecasting for Power System Operator}, JOURNAL = {Energies}, VOLUME = {16}, YEAR = {2023}, NUMBER = {3}, ARTICLE-NUMBER = {1243}, URL = {https://www.mdpi.com/1996-1073/16/3/1243}, ISSN = {1996-1073}, 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). 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%.}, DOI = {10.3390/en16031243} }Impacts of Tariffs on Energy Conscious Behavior with Respect to Household Attributes in Saudi Arabia
@Article{en16031458, AUTHOR = {Nahiduzzaman, Kh Md and Said Abdallah, Abdullatif and Moradzadeh, Arash and Mohammadpour Shotorbani, Amin and Hewage, Kasun and Sadiq, Rehan}, TITLE = {Impacts of Tariffs on Energy Conscious Behavior with Respect to Household Attributes in Saudi Arabia}, JOURNAL = {Energies}, VOLUME = {16}, YEAR = {2023}, NUMBER = {3}, ARTICLE-NUMBER = {1458}, URL = {https://www.mdpi.com/1996-1073/16/3/1458}, ISSN = {1996-1073}, 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 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.}, DOI = {10.3390/en16031458} }A New QFD-CE Method for Considering the Concept of Sustainable Development and Circular Economy
@Article{en16052474, AUTHOR = {Siwiec, Dominika and Pacana, Andrzej and Gazda, Andrzej}, TITLE = {A New QFD-CE Method for Considering the Concept of Sustainable Development and Circular Economy}, JOURNAL = {Energies}, VOLUME = {16}, YEAR = {2023}, NUMBER = {5}, ARTICLE-NUMBER = {2474}, URL = {https://www.mdpi.com/1996-1073/16/5/2474}, ISSN = {1996-1073}, 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 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.}, DOI = {10.3390/en16052474} }From Secondary Biomass to Bio-Methanol through CONVERGE Technology: An Environmental Analysis
@Article{en16062726, AUTHOR = {Galusnyak, Stefan Cristian and Petrescu, Letitia and Chisalita, Dora Andreea and Cormos, Calin-Cristian and Ugolini, Marco}, TITLE = {From Secondary Biomass to Bio-Methanol through CONVERGE Technology: An Environmental Analysis}, JOURNAL = {Energies}, VOLUME = {16}, YEAR = {2023}, NUMBER = {6}, ARTICLE-NUMBER = {2726}, URL = {https://www.mdpi.com/1996-1073/16/6/2726}, ISSN = {1996-1073}, 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 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.}, DOI = {10.3390/en16062726} }@Article{en16083473, AUTHOR = {Palander, Teijo}, TITLE = {Data-Driven Internal Carbon Pricing Mechanism for Improving Wood Procurement in Integrated Energy and Material Production}, JOURNAL = {Energies}, VOLUME = {16}, YEAR = {2023}, NUMBER = {8}, ARTICLE-NUMBER = {3473}, URL = {https://www.mdpi.com/1996-1073/16/8/3473}, ISSN = {1996-1073}, 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 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.}, DOI = {10.3390/en16083473} }Analysis of the Level of Efficiency of Control Methods in the Context of Energy Intensity
@Article{en16083507, AUTHOR = {Pacana, Andrzej and Czerwińska, Karolina and Ostasz, Grzegorz}, TITLE = {Analysis of the Level of Efficiency of Control Methods in the Context of Energy Intensity}, JOURNAL = {Energies}, VOLUME = {16}, YEAR = {2023}, NUMBER = {8}, ARTICLE-NUMBER = {3507}, URL = {https://www.mdpi.com/1996-1073/16/8/3507}, ISSN = {1996-1073}, 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 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.}, DOI = {10.3390/en16083507} }
Please consider this special issue and feel free to contact me if you have a relevant manuscript to publish.
