Efficient Energy Production Research Articles

Development of A Man-Machine Interface for Managing Photovoltaic Energy: Applied Study

Objective: Develop and evaluate a human-machine interface (HMI) that integrates advanced monitoring, forecasting, and management functionalities for photovoltaic solar energy systems, aiming to optimize energy production and operational efficiency. Theoretical Framework: This study is based on concepts of modeling and simulation, solar energy management, and problem-solving methodologies such as Soft System Methodology (SSM). Method: An applied approach was adopted using modeling, simulation, and statistical analysis techniques. The research included a bibliographic review in scientific databases, a case study, and SSM to organize and solve complex problems. 121 digital solar energy platforms in Brazil were analyzed to define the interface requirements. The interface was developed with React JS, Axios, Bootstrap v5, Apache Echarts, HTML, CSS, JavaScript, and Python libraries for forecasting models. Results and Discussion: The interface, named "Solar Smart Manager," enables efficient monitoring and management of energy production using critical data such as temperature, time of day, and solar irradiation. Tests in a real operational environment demonstrated improvements in energy management, incident response, and preventive maintenance. The functionality of validating solar radiation incidence data represents a significant contribution to the energy sector, promoting sustainability and innovation. Research Implications: The practical and theoretical implications of this research provide insights into the efficient and optimized management of photovoltaic solar energy systems, contributing to a better understanding and optimization of available solar resources. Originality/Value: This study contributes to the literature by developing an innovative interface that improves operational efficiency and solar energy management. The relevance and value of this research are evidenced by its positive impact on the energy sector, promoting sustainability and innovation.

Rational Design and Synthesis of CO2 Reduction Reaction Catalysts of High Faradaic Efficiency Toward C2 + Chemical Conversions

Carbon dioxide emission from fossil fuel combustion poses a major threat to global environment and ecological systems.Carbon capture, sequestration and conversion technologies are widely pursued as possible solutions to mitigate negative impact by CO2. The electrochemical CO2 reduction reaction (CO2RR) to fuels and chemicals using renewable electricity offers attractive “carbon-neutral” and “carbon-negative” mitigation strategies. Various catalysts have been investigated as the electrocatalysts for CO2RR. Key challenges facing the current catalyst and electrolyzer designs include insufficient energy efficiency and low single product selectivity.CO2RR to C2+ chemicals represent a highly important area for CO2 reduction and utilization. For example, ethanol, ethylene, propanol, etc. are among the most produced chemicals by the industry and are widely used for various applications. Using CO2 as raw material for chemical production through electrocatalysis not only improves the carbon cycling, but also reduces the overall emissions from chemical production. While CO2RR via two proton-electron pairs (PEPs), such as the conversion of CO2 to CO or formate, have been proven high selective with fast kinetics, conversions to C2+ chemicals are significantly more difficult due to the rapid escalation of required PEPs to much higher numbers (for example, 12 for ethanol and 18 for propanol), in addition to C-C bond coupling. The increased PEPs substantially complicate the conversion by required multiple steps along the electrochemical coordinate, leading to a high probability of branching reactions and low single product Faradaic efficiency (FE).To address these challenges, the design criteria for CO2RR to C2+ chemicals should be based on high uniformity of active center for directing identical catalytic path and suppressing competing reactions, strong catalyst-reactant binding in capturing the transient species through multiple PEP transfers, and microenvironment with nanoconfinement for retaining reaction intermediates during extended catalytic processes.Recently, we developed a new amalgamated lithium metal (ALM) synthesis method of preparing highly selective and active CO2RR catalyst and achieved > 90% FE for conversion of CO2 to ethanol [1]. Our have since expanded the approach to other C2 + chemicals including acetate, acetone, glycerol, isopropanol, etc., all with FE at or higher than 80%. To better formulate our catalyst design strategy, we not only measured the CO2RR performance over a variety of electrocatalysts, but also investigated their activity-structure relationship through advanced material characterizations, combined with the first-principle computation. We found many interesting properties uniquely associated to CO2RR mechanism and kinetics, such as catalyst-size and electro-potential modulated single selectivity. In this presentation, we will share our recent discoveries and future perspective on the development of highly selective CO2RR catalysts for C2+ chemical conversions. Acknowledgement: This work is supported by U. S. Department of Energy, Office of Energy Efficiency and Renewable Energy - Industrial Efficiency & Decarbonization Office and by Office of Science, U.S. Department of Energy under Contract DE-AC02-06CH11357.[1] “Highly selective electrocatalytic CO2 reduction to ethanol by metallic clusters dynamically formed from atomically dispersed copper” Haiping Xu, Dominic Rebollar, Haiying He, Lina Chong, Yuzi Liu, Cong Liu, Cheng-Jun Sun, Tao Li, John V. Muntean, Randall E. Winans, Di-Jia Liu and Tao Xu, (2020) Nature Energy, 5, 623–632

Identification of Local Overpotentials and Their Spatial Distributions in Polymer Electrolyte Water Electrolyzers

Polymer electrolyte water electrolyzers (PEWEs) are pivotal in the transition to a clean energy economy, promising green and efficient hydrogen production. Understanding the complexities of overpotentials within PEWEs is crucial for optimizing their performance and efficiency1. For PEWEs to attain the highest possible system efficiency, considering a specific material set and operating parameters, meticulous design is imperative. The goal is to achieve a high reaction rate that is not only optimal but also uniform across the entire active surface area of the electrodes. This uniformity should ideally be maintained with minimal sensitivity to variations in operating conditions.This study focuses on the contribution of different overpotential categories and reveals their spatial variations through in-situ analysis to understand and overcome the issues caused by non-uniformity in reaction. Employing a segmented cell and printed circuit board (PCB) approach2, current density distribution (CDD) and distributed area-specific resistance (DASR) measurements are obtained, enabling a detailed examination of the mass transport overpotentials (as shown in Figure 1). Typically, in the fuel cell community, modeling mass transport limitations is thought of as diffusion-limited transport3. In the anode porous transport layer (PTL), oxygen accumulation (i.e. product transport limitation) can cause membrane dry-out and an increase in ASR4. The experimental results elucidate the intricate relationship between ohmic and mass transport losses, emphasizing the deep coupling induced by the two-phase nature of the system. Figure 1 shows that, as mass transport limitation grows, the highest increase in overpotential comes from mass transport-coupled ohmic losses. Beyond conventional diffusion limited mass transport losses, bubble dynamics significantly impact local hydration and ASR, highlighting the need for a holistic approach in device optimization5.This research contributes to the understanding of PEWE dynamics, providing a basis for informed engineering decisions. By unraveling the intricate interdependencies between overpotentials, this work empowers the efficient utilization of expensive catalyst materials. Moreover, it offers insights into mitigating the challenges associated with bubble removal, accomplishing this with lower pumping requirements that scale non-linearly. These insights enrich both theoretical understanding and are paramount for enhancing system efficiency and economic viability of PEWEs, thereby advancing the prospect of sustainable hydrogen production. Reference N. Danilovic and I. Zenyuk, Electrochem. Soc. Interface, 30 (2021).F. H. Roenning, A. Roy, D. S. Aaron, and M. M. Mench, J. Power Sources, 542, 231749 (2022) https://linkinghub.elsevier.com/retrieve/pii/S037877532200742X.A. Z. Weber et al., J. Electrochem. Soc., 161, F1254–F1299 (2014) https://iopscience.iop.org/article/10.1149/2.0751412jes.C. Immerz, B. Bensmann, P. Trinke, M. Suermann, and R. Hanke-Rauschenbach, J. Electrochem. Soc., 165, F1292–F1299 (2018).P. Satjaritanun et al., iScience, 23, 101783 (2020) https://doi.org/10.1016/j.isci.2020.101783. Figure 1

(Invited) Achieving Robustness and Economic Viability in CO2 Electrolysis Scale-up: Key Results from RenewCO2

RenewCO2 is commercializing processes to convert carbon emissions from chemical manufacturing, which contributes 274 million metric tons of CO2eq annually in the U.S. alone. Our pioneering electrocatalytic Carbon Utilization Technology (eCUT) is adept at upcycling CO2 from hard-to-decarbonize industrial sources. By synthesizing commodity chemicals from CO2 emissions, eCUT promises high energy efficiency while producing carbon-negative materials. The critical innovation of RenewCO2’s eCUT process is deploying catalysts designed with readily available metals, optimizing the process to reduce CO2 into multi-carbon chemicals selectively with impressive energy efficiency.RenewCO2 has significantly advanced CO2 electrolyzer scale-up, focusing on demonstrating process robustness and economic feasibility. Targeting liquid products such as monomers (glycols), RenewCO2 has demonstrated cost-advantaged process economics compared to the incumbent petrochemical and bio-derived routes. We present detailed findings on the enhanced performance of our proprietary catalysts, which have shown remarkable endurance under prolonged operational conditions (1000 h of operation) in a scaled-up stack containing five 500 cm2 electrodes. The RenewCO2 team has demonstrated consistent electrolyzer performance with minimal degradation over extended periods. We also delve into improvements in energy efficiency, showcasing how these enhancements contribute to the overall process economics.The purification process is crucial in making carbon-negative materials from CO2 that do not require a green premium. In this regard, RenewCO2 has achieved remarkable progress in demonstrating a highly energy-efficient product purification. In collaboration with Argonne National Lab, we have developed an advanced electrochemical separation method that removes ionic species from the products to comply with strict industrial standards and increase their market value. A key development in this area is our innovative approach to electrodeionization, which combines ion-exchange membranes with electrically active media. This method exhibits low energy consumption, robustness, and simple operation. These advancements in the purification process are integral to our system’s overall efficiency and are pivotal in our pursuit of creating economically viable and sustainable electrochemical manufacturing solutions.On the economic front, we provide an in-depth analysis of the cost implications of scaling up. This includes a breakdown of the capital and operational costs, along with a comparative analysis with existing technologies. We discuss the main factors contributing to product cost and strategies to minimize them, such as the integration of low-cost materials and judicious choice of operating conditions.Our aim is to provide a transparent and comprehensive overview of the challenges and the state-of-the-art in scaling up CO2 electrolysis to liquid products, offering valuable insights for the scientific community and stakeholders in the field.

The Impact of Additives on Gaseous Pollutants from the Combustion of Various Solid Fuels

This article compares the emission of gaseous pollutants such as CO2, CO, NO, SO2, and HCl emissions from the combustion of the selected most popular solid fuels in a low-power boiler. The process was carried out under controlled conditions on a laboratory stand equipped with a Moderator Unica Vento Eko 25 kW boiler. Solid fuels were selected for comparison, such as hard coal with granulation above 60 mm, hard coal with a granulation of 25–80 mm, hard coal with a granulation of 8–25 mm, wood pellets, and mixed firewood. The experiment was carried out in two stages. In stage 1, previously selected solid fuels were combusted under controlled repeatable conditions, while simultaneously measuring gaseous components in the exhaust gases in real time. On the other hand, the second stage involves the combustion of the same fuels under the same conditions with combustion additives that modify the combustion process in terms of reducing the emission of pollutants. At the same time, in the second stage, gaseous components in the exhaust gas were also measured in real time. The experiments carried out have shown that, in addition to the additive, a testing system should be used to assess the profitability and improve the efficiency of energy production and distribution after using a given additive for fuel combustion. Implementation of the use of solid fuel activators on a common scale should also entail research on the emission of dioxins and furans, which may be emitted in increased amounts under the influence of some components contained in combustion modifiers.

Thermal integration process for waste heat recovery of SOFC to produce cooling/H2/power/freshwater; cost/thermal/environmental optimization scenarios

High-temperature fuel cells (HT-FCs) indeed hold significant promise for enhancing energy systems' efficiency and environmental sustainability. Consequently, there is growing interest in exploring and developing innovative strategies to optimize the integration of heat recovery processes with HT-FC technology. The current research presents an innovative and environmentally friendly poly-generation unit that integrates advanced subprocesses to generate essential products. These final products include electricity, refrigeration, pure water, and hydrogen. This study signifies a significant step towards sustainable and efficient energy production while meeting diverse needs for different utilities. The design unit incorporates a novel modified dual ejector-based organic flash cycle, reverse osmosis water purification, and a water electrolyzer for hydrogen extraction, all integrated with a high-temperature solid oxide fuel cell. Energy, exergy, economic, and environmental (4E) analysis is conducted to thoroughly evaluate the proposed plan. Furthermore, a comprehensive parametric analysis and sensitivity study are performed to pinpoint the key design parameters of the poly-generation unit. To attain the optimal operational status of the poly-generation unit, a three-objective NSGA-II optimization technique is employed to fine-tune the system's performance within the exergy-cost-environmental framework. This approach aims to strike a balance between maximizing efficiency, minimizing costs, and reducing environmental impact for sustainable operations. In addition, a net present value analysis spanning a 20-year timeframe was conducted to assess the profitability of the devised unit. Based on the findings, it is evident that the sensitivity index of the fuel cell operating temperature carries substantial importance, registering a notable value of 0.60. Additionally, the optimization outcomes reveal the system's enhanced performance metrics: an exergetic efficiency of 41.11 %, a unit cost of 58.96 $/GJ, and a carbon dioxide emission reduction rate of 418 kg/MWh. Also, the unit's payback period is shortened from 11.33 years to 8.817 years. Moreover, it improved the unit's sustainability index and net present value, raising them from 1.544 to 4.98 M$ to 1.66 and 7.38 M$.

Promotion of CO2 Electroreduction on Bismuth Nanosheets with Cerium Oxide nanoparticles.

Formic acid (HCOOH) is a highly energy efficient product of the electrochemical CO2 reduction reaction (CO2RR). Bismuth-based catalysts have shown promise in the conversion of CO2 to formic acid, but there is still a great need for further improvement in selectivity and activity. Herein, we report the preparation of Bi nanosheets decorated by cerium oxide nanoparticles (CeOx) with high Ce3+/Ce4+ ratio and rich oxygen vacancies. The CeOx nanoparticles affect the electronic structures of bismuth, enhance CO2 adsorption, and thus promote the CO2RR properties of Bi nanosheets. Compared with elemental Bi nanosheets, the hetero-structured CeOx/Bi nanosheets exhibit much higher activity over a wide potential window, showing a current density of 16.1 mA cm-2 with a Faradaic efficiency of 91.1% at -0.9 V vs. reversible hydrogen electrode.

A game theoretic approach for tradable white certificates regarding energy rebound and government intervention

In recent decades, many government administrations have tracked energy efficiency programs (EEPs) against environmental concerns. This was done so that it could potentially be useful as a supportive mechanism for smart technologies applied under the smart city concept. To facilitate this, tradable white certificates (TWCs) have been implemented as popular financial instruments used by energy-intensive sectors to boost cleaner production. In this study, we address an industrial EEP development with a TWC instrument as a multi-agent problem. We study this problem for the first time in the context of a supply chain that includes a manufacturer, an energy producer, and household energy consumers. Furthermore, we explore a new monopolistic pricing model for energy services and energy-efficient products, regarding the rebound effect, energy consumption, and social welfare. Additionally, we discuss two revenue-cost-sharing contracts and compare them as contracts using a comprehensive parametric and experimental analysis. The results show that the second proposed contract has some advantages over the first one. However, the second contract leads to less production than the first one does, while at the same time leading to less social welfare. Also, the findings suggest that the second investigated contract is a more appropriate instrument for the obligated parties than the first one when the aim is to improve the performance of TWC schemes. These findings can provide better circumstances for governance to optimize the critical parameters’ level on TWC schemes with the lowest analytical cost.Graphical abstract

Construction of bifunctional S-doped N-deficient carbon nitride for efficient simultaneous photocatalytic H2O2 synthesis and BPA degradation

The attainment of simultaneously efficient clean energy production and environmental pollution treatment has been a research focus yet still challenging. Herein, S-doped N-deficient carbon nitride (S-g-C3N4-N) is ingenously synthesized for photocatalytic two-electron oxygen reduction reaction (2e− ORR) for hydrogen peroxide (H2O2) generation and simultaneous degradation organic pollutants (BPA). Experimental/theoretical studies show that S doping and N defects endow S-g-C3N4-N with improved light absorption, carrier separation and transport, and O2 adsorption and activation capacity compared to g-C3N4. On this basis, S-g-C3N4-N has an excellent H2O2 yield (221.68 μM) and BPA degradation rate (0.0596 min−1), far exceeding the pristine g-C3N4. More notably, the existence of BPA synergistically improves the production of H2O2, which is attributed to the fact that BPA degradation consumes many photogeneration holes and promotes carrier separation, thus boosting 2e− ORR to synthesize H2O2. Our study offers novel guidance for efficient photocatalytic generation of H2O2 and the simultaneous breakdown of organic pollutants.

Impact of dust and temperature on photovoltaic panel performance: A model-based approach to determine optimal cleaning frequency

Enhancing the reliability of photovoltaic (PV) systems is of paramount importance, given their expanding role in sustainable energy production, carbon emissions reduction, and supporting industrial growth. However, PV panels commonly encounter issues that significantly impact their performance. Specifically, the accumulation of dust and the rise in internal temperature lead to a drop in energy production efficiency. The primary issue addressed in this paper is using mathematical modeling to determine the optimal cleaning frequency. This paper first focuses on stochastic modeling for dust accumulation and temperature changes in PV panels, considering varying environmental conditions and proposing a model-based approach to determine the optimal cleaning frequency. Dust accumulation is described using a Non-homogeneous compound Poisson process (NHCPP), while temperature evolution is modeled using Markov chains. Within this framework, we consider the impact of wind speed and rainfall on dust accumulation and temperature. These factors, treated as covariates, are modeled using a two-dimensional time-continuous Markov chain with a finite state space. A Condition-based cleaning policy is proposed and assessed based on the degradation model. Optimal preventive cleaning thresholds and cleaning frequency (periodic and non-periodic) are determined to minimize the long-term average maintenance cost. The gain achieved by non-periodic inspections compared to periodic inspections ranges from 3.83% to 9.37%. Numerical experiments demonstrate the performance of the proposed cleaning policy, highlighting its potential to improve PV system efficiency and reliability.

Edge-platforms based decision-support approach for solar panels inspection with YOLOv8 deep neural-network

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>This paper presents an innovative AI-based method for autonomous inspection, designed to enhance energy production efficiency by optimizing cleaning strategies for soiled photovoltaic panels, using advanced artificial intelligence algorithms to analyze panel conditions and environmental factors in real-time, allowing for targeted cleaning interventions. Based on the advanced YOLOv8 deep learning algorithm and computer vision approach, the proposed method offers distinct advantages in real-time detection and classification of various types of soiling and dust accumulation compared on solar panels to traditional methods, and underwent satisfactory testing across diverse scenarios. The NVIDIA Jetson Nano, the Raspberry Pi4 embedded devices, and the Raspberry Pi4 combined with NCS2 accelerator are used for implementing our approach. A comparison aims to provide a detailed exploration of the most suitable embedded platform for deploying our advanced system was discussed. This comparison considers processing speed and accuracy, energy consumption, and overall performance in executing the computationally intensive tasks. The results demonstrate that our model achieves high accuracy in detecting soiling and enhancing the model's detection speed. With an average precision of 99.5%, this approach ensures accurate fault identification, underscoring the effectiveness of computer vision using deep learning algorithms for detection tasks across a wide range of scenarios.</p></div></div></div>

An improved capacitance-resistance model for analysing hydrogen production with geothermal energy utilisation

The integration of geothermal and hydrogen energy through advanced predictive modeling not only enhances energy production efficiency but also contributes significantly to the global transition towards renewable energy sources. This study commences with the establishment of a geothermal hydrogen production model using Aspen Plus, incorporating physical constraints, derived from the Capacitance Resistance Model (CRM) model. Refinements to Long Short-Term Memory (LSTM) neural networks based on historical data and CRM constraints tailor them for geothermal reforming-based hydrogen production. Production analysis indicates that the hybrid CRM-LSTM model adeptly predicts heat and hydrogen production, improving system performance. The approach demonstrates superior accuracy, with hydrogen production predictions deviating by less than 2%. The comparison highlights the hybrid model’s advantage in handling nonlinear characteristics but it also shows the hybrid model requires longer training times. Sensitivity analysis reveals significant implications for investment decisions, with CRM predicting a 26-year cost recovery period under standard conditions, potentially underestimating actual outcomes by over eight months. Such discrepancies underscore the importance of accurate predictive models in guiding investment decisions for sustainable energy projects. This model contributes to achieving sustainable development goals by integrating geothermal and hydrogen energy, advancing the transition towards renewable and environmentally friendly energy sources.

Energy-efficient production of sustainable industrial process heat with a parabolic dish-conical cavity solar collector

Industrial decarbonization in response to global carbon neutrality requires vast process heat to be supplied in a low-carbon manner. Solar energy is regarded as one of the most promising alternatives to fossil fuel applied for heat-driven industrial processes, however, lack of effective approaches to improve the solar-thermal conversion efficiency is a longstanding issue. Here, we propose an efficient approach to produce industrial process heat based on a novel concentrated solar collector, mainly consisting of a parabolic dish concentrator and a conical cavity receiver. Results showed that the collector was competent to supply the process heat meeting various low-temperature industrial occasions, and there was an optimum flow rate to achieve a trade-off between the outlet temperature of working fluid and thermal efficiency of the solar collector. The thermal efficiency of the proposed solar collector was 2.7%–27.5% higher than that of most other existing collectors used for production of low-temperature process heat, and such a superior solar-thermal conversion efficiency was a consequence of the reduction in energy losses between the solar receiver and surroundings as confirmed by the comprehensive energy analysis performed in this study.

An integrated model for flexible simulation of biomass combustion in a travelling grate-fired boiler

Grate-fired boiler is of considerable interest for burning biomass owing to its reduced sensitivity to variations in fuel composition and size. However, grate-fired technology exhibits unsatisfactory performance in terms of combustion efficiency, contaminant emissions, and flame stability. CFD modelling can provide reasonable optimizations to overcome these drawbacks and ultimately achieve clean and efficient energy production. Conventional approach simulates the fuel-bed and freeboard combustion separately using an in- and over-bed coupling procedure, where data of the gases leaving from the packed-bed top and the radiative heat flux emitted by the high-temperature flame onto the bed cannot be interacted in real-time. This may cause distinct deviations in the calculations. In this paper, a three-dimensional (3D) full-scale integrated model is developed for the simulation of grate-fired boiler, in which the intensive combustion of both the freeboard and fuel-bed is solved in one scheme using the Eulerian-Lagrangian method. The gas phase is considered as a continuous medium and calculated by the Eulerian model, while the solid particles are considered as a discrete phase and tracked via the Lagrangian method. The reliability of the model is well verified through various field measurements and observations. Results shows that the key parameters are non-uniformly distributed along the grate width, i.e., the aerodynamic and combustion environment is poor at the vicinity of water-cooled walls compared with that at the furnace center. This results in a significant lag in volatile gases release and char oxidation near the water-cooled wall on both sides, which finally causes obvious differences in the distribution of temperature and species. For instance, the peak of CO concentration at the center is 12.6 % at 3.3m from the feed entrance, while that near the water-cooled wall is 8.6 % around 3.5m. The char burnout ratio in the bottom ash can be accurately calculated by this model, with a negligible relative error between the simulation of 81.97 % and the measurement of 82.50 %. It also provides a novel method for the calculation of fly ash particles by using size-grouped and non-spherical particles. Small-size particles in the vicinity of the feed inlet, as well as a part of the particles burning at the end of the grate are entrained into the freeboard by the primary air supplied beneath the grate and the high-momentum secondary air on the bed top. This model can provide valuable theoretical guidance for optimizing the refined distribution of fuel and air in grate-fired power plants and mitigating irregular deposition (corrosion) on heat exchangers and water-cooled walls caused by fly ash.

Modular Digital Twin – an approach for generating and exploiting product sustainability information towards service-oriented business models

ABSTRACT Companies receive numerous customer inquiries and regulatory requirements while attempting to offer new services to enhance sustainability, leading in total to an increase in the number of product-related requirements. Digital Twins serve as enablers for meeting these requirements, whereas new challenges arise for companies through the introduction of Digital Twins. Digital Twins represent an order-specific configuration and must be structured differently according to requirements and thus as modularly as possible. This paper aims to provide a systemic approach with a concept for a Modular Digital Twin for generating and exploiting Product Sustainability Information to leverage sustainability potentials towards Service-oriented Business Models. The approach is concretised by means of a selected use case regarding energy-efficient production control of an automated model factory. We can show with this use case that the systemic approach with the concept of a Modular Digital Twin is applicable for energy consumption of machinery motors' simulation and for the calculation of an optimised motor speed.

Harnessing the potential of agriculture biomass: reuse, transformation and applications in energy and environment.

Biomass, an organic matter, has gained worldwide attention due to the overconsumption of fossil fuels. Biomass has emerged as a new alternative resource with implications for efficient energy production, environmental benefits and socio-economic impacts. According to the World Bioenergy Association, biomass has accounted for 14% of the energy supply in 2016 and is expected to provide 44% of the energy demand by 2030. This literary endeavour comprises insights into past developments, including biomass types, characterization methods and conversion technologies. This review article aims to facilitate a deeper understanding of agriculture biomass utilization and its significance in achieving sustainable development goals by analysing the latest research findings. Moreover, the emerging role of biosensors in optimizing biomass utilization and monitoring environmental impacts has been documented. The scope embraces the vast realm of bioenergy production, environmental mitigation and the generation of valuable by-products. In conclusion, portraying biomass conversion technologies as the transition towards cleaner, renewable energy, the potential benefits and challenges extend beyond energy production, encompassing effective agricultural residue management and the creation of valuable by-products. This review will guide the researchers and stakeholders towards a deeper understanding of the transformative potential embedded in biomass conversion processes for a sustainable and cleaner energy future.

Processing of fully dense and high conductivity W[sbnd]20Cu composite via copper melt infiltration into a pressed tungsten wire skeleton

The current study proposed melt infiltration into entangled pressed wire as a proper method for rapid and energy-efficient production of tungsten‑copper composites. The process includes wire packing through different strategies, pressing, and infiltration steps. First, a predesigned wire structure through different strategies of simple, spiral, cylindrical, and spherical is packed and pressed to form a permeable tungsten skeleton. Then, by infiltrating molten copper under a dry hydrogen atmosphere into the porous preforms, the W20Cu composite is fabricated and characterized. An almost fully dense sample with excellent hardness of 238.1 HV1 and high conductivity of 58.64% IACS in W20Cu composite was achieved. These values are almost 25% and 50% higher than that from the conventional melt infiltration method, respectively. It was seen that the packing strategy is so effective in achieving the best properties where the processed composite by spherical packing strategy exhibits better density, conductivity, and hardness than the sample produced via the other three packing strategies. However, all the properties of the melt infiltration into entangled pressed wire processed composite via all packing strategies are higher than those processed via conventional methods. These excellent properties could be attributed to the mostly bonded tungsten phase, high purity, pore-free, and suitable distribution of tungstens which are a result of this process characteristics. Besides, lower cycle time and energy consumption are other advantages of the new process compared to conventional melt infiltration via powder metallurgy. This approach presents a promising prospect for future industrial applications.

Pricing and Inventory Decisions for the Apparel Industry Under a Carbon Neutrality Target with Green Investments and Recycling Efforts

The apparel industry is the second largest source of pollution globally, following only the petrochemical industry in terms of environmental pollution caused by production, sales, and consumption processes. Low-carbon, environmentally friendly, and energy-efficient production and consumption methods are crucial pathways for the apparel industry to achieve carbon neutrality targets. Therefore, considering green investments in apparel products and the recycling of discarded apparel, this paper investigated the inventory and pricing optimization decisions of apparel enterprises within the context of a carbon neutrality target. The results indicated that green investment or recycling can effectively increase the total profits of the apparel brand. Due to the cumulative effect of comprehensive environmental strategies, apparel brands achieve greater profits when simultaneously adopting both green investments and recycling efforts compared to implementing either one alone. Green investments focus on reducing the environmental impact during production by minimizing resource consumption and emissions from the source, while recycling emphasizes product reutilization, effectively extending the utilization cycle of resources. By implementing these two strategies, brands not only reduce the negative environmental impacts during production but also maximize resource reuse throughout the product lifecycle.

Achieving energy efficiency and sustainability in galvanization process: A case study on Indian tower manufacturing industry

Abstract There is a significant gap between India’s energy production and its demand. Dependency on energy imports to meet the energy requirement affects the economic growth & overall development. The manufacturing sector consumes a substantial portion of the country’s energy but also holds considerable potential for reducing consumption. This paper presents a case study on an Indian tower manufacturing industry, aiming to achieve energy efficiency and sustainability in the galvanization process through the application of the statistical tool Design of Experiments (DOE). By systematic implementation of 22 factorial model of DOE, the study identified that the excessive zinc coating thickness is the major cause of energy consumption in hot-dip batch galvanization process (HDBGP). It was found that average LPG consumption is minimized at moderate coating thickness. Applying DOE resulted in a 38.67% reduction in LPG consumption when the zinc coating thickness was maintained at a moderate level in HDBGP. DOE provides a systematic mechanism based on experimental data to identify potential causes of problems and offers statistical validation of results. This research focuses on creating an energy-efficient production system and demonstrates the practical application of statistical tools and techniques to achieve sustainable competitive advantages. Highlights Experimental work using the Design of Experiments tool identified excessive zinc coating thickness as the primary cause of high energy consumption in the hot-dip batch galvanization process. Validation of optimized parameters showed that average energy consumption is minimized at moderate coating thickness, resulting in a 38.67% reduction in energy usage. Design of experiments tool provides a systematic mechanism based on experimental data to identify potential causes of problems and offers successful validation of results. Discussion While the significant reduction in LPG consumption enhances energy efficiency and sustainability, excess reduction in coating thickness may compromise the quality and durability of galvanized products, potentially leading to higher long-term costs for maintenance and replacements. Additionally, the focus on statistical tools like DOE alone could divert attention from exploring alternative potential energy-saving technologies and methods that might offer greater benefits. Therefore, it is also essential to consider the broader implications and potential limitations of various approaches in the quest for sustainability and competitive advantage suitable for the existing manufacturing setups. Graphical abstract

Energy control and block performance optimization of bench blasting

In this study, rock-like model blast tests with varying toe burdens are conducted and the evolution of blast-induced fractures is analyzed using the digital image correlation (DIC) method. In addition, post-blast fragmentations are image-processed to quantitatively investigate the blasting performance. A three-dimensional numerical model is developed and validated against the experimental results. The effects of blasthole inclination (70°, 80°, and 90°) and short delays on fragment size distribution (FSD) are numerically investigated. Field trials are conducted to verify the numerical results. The experimental results reveal that the overall fragment size increases significantly in proportion to the toe burden. The explosion energy utilization rates range from 3.56 % to 13.16 %, with a tendency to initially increase and subsequently decrease. The numerical results demonstrate that fragments are more evenly distributed with a blasthole inclination of 70°, resulting in a remarkable improvement in rock fragmentation compared to a vertical blasthole. Compared to the toe burden, the influence of short delays on concrete fragmentation is insignificant. The field test results indicate that a narrower FSD range and stable pit walls are achieved by utilizing inclined blastholes in bench blasting. This study provides theoretical guidance for optimizing bench blasting parameters, which has practical significance for improving explosive energy utilization and production efficiency in open-pit mines.