Reduce Energy Consumption Research Articles

Square-wave pulsed potential driven electrocatalytic degradation of 4-chlorophenol using Fe-Ni/rGO/PPy@NF three dimensional electrode

To develop an energy-efficient system for the removal of chlorinated organic pollutants, Fe-Ni/reduced graphite oxide/polymerized polypyrrole@nickel foam was constructed as a catalytic cathode for pulsed electrocatalytic degradation, where cathode-catalyzed production of hydrogen radicals (H*) and hydroxyl radical (·OH) generated at the anode led to dechlorination of 4-chlorophenol (4-CP), and dechlorination products were mineralized and degraded under the action of·OH. When energy was continuously supplied to the reaction system in the constant potential mode, the 4-CP concentration near the electrode was insufficient, limiting the reaction rate. Conversely, in the square-wave pulsed potential mode, mass transfer limitations were mitigated, significantly enhancing reaction efficiency and reducing energy consumption. At −1.2 V (vs. Ag/AgCl), the 4-CP removal efficiency reached 93.79 % in the pulsed potential mode, surpassing the constant potential mode's performance of 81.40 %. The synergistic periodic oscillation of the potential, direct electron transfer, and catalytic generation of active free radicals in the pulsed potential mode reduced intermediate concentrations and increased 4-CP mineralization, while the degradation pathway remained unchanged. This research presents a method for the efficient treatment of chlorinated organic pollutants in water using pulsed electrocatalytic degradation.

Monitoring of indoor environmental quality and acoustic comfort in university classrooms

Indoor Environmental Quality (IEQ) in university classrooms affects students' well-being, comfort and learning performance, thus its evaluation is strongly needed. This work presents an in-field study on the acoustic monitoring, along with thermal, visual and air quality domains, in four recently built university classrooms of Politecnico di Torino. The monitoring was performed in the spring/summer and winter periods, through wall mounted multi-sensor devices. While the monitoring was ongoing, students attending lessons were asked to answer an online anonymous questionnaire on comfort perception, which included questions regarding the four domains and additional personal information. Results show that students are more satisfied with the overall conditions in classroom 3P and more dissatisfied with the overall conditions in classroom 4P. The less satisfying domains are the thermal and air quality ones, whereas the more satisfying are the acoustic and visual ones. Furthermore, at the increasing of TVOC and CO2 concentrations the overall satisfaction decreases. The final aim of this project, based on the monitoring results, is the optimization of the building systems management to reduce energy consumptions and ensure occupants' well-being and comfort.

Image acquisition based on computer vision technology for optimizing thermal energy in building environments and simulating VR interior design

With the acceleration of urbanization, the problem of building energy consumption has become increasingly prominent, and the construction of low energy consumption and high comfort building environment has become a hot research field. The purpose of this study is to explore the use of computer vision technology for image acquisition, optimize the thermal energy management in the building environment through intelligent analysis, and improve the energy efficiency and comfort of interior design. Through the establishment of image acquisition system based on computer vision, the indoor and outdoor environment is monitored in real time. A deep learning algorithm is used to process the acquired images and extract the thermal energy characteristics of the buildings. Combined with virtual reality (VR) technology, a 3D model of interior design was constructed to simulate the impact of different design schemes on energy consumption. The experimental results show that the real-time data obtained by computer vision technology can effectively identify the heat energy distribution in the building, and provide optimization suggestions for interior design. Simulation results of different design options show that the optimized design reduces energy consumption while improving occupant comfort. Therefore, computer vision technology shows a good application prospect in the thermal energy optimization of building environment. By combining VR interior design simulation, the technology not only improves the accuracy of thermal management, but also provides architects with more information-based decision support and promotes the development of smart buildings.

Deep Recyclable Trash Sorting Using Integrated Parallel Attention.

Sorting recyclable trash is critical to reducing energy consumption and mitigating environmental pollution. Currently, trash sorting heavily relies on manpower. Computer vision technology enables automated trash sorting. However, existing trash image classification datasets contain a large number of images without backgrounds. Moreover, the models are vulnerable to background interference when categorizing images with complex backgrounds. In this work, we provide a recyclable trash dataset that supports model training and design a model specifically for trash sorting. Firstly, we introduce the TrashIVL dataset, an image dataset for recyclable trash sorting encompassing five classes (TrashIVL-5). All images are collected from public trash datasets, and the original images were captured by RGB imaging sensors, containing trash items with real-life backgrounds. To achieve refined recycling and improve sorting efficiency, the TrashIVL dataset can be further categorized into 12 classes (TrashIVL-12). Secondly, we propose the integrated parallel attention module (IPAM). Considering the susceptibility of sensor-based systems to background interference in real-world trash sorting scenarios, our IPAM is specifically designed to focus on the essential features of trash images from both channel and spatial perspectives. It can be inserted into convolutional neural networks (CNNs) as a plug-and-play module. We have constructed a recyclable trash sorting network building upon the IPAM, which produces an acuracy of 97.42% on TrashIVL-5 and 94.08% on TrashIVL-12. Our work is an effective attempt of computer vision in recyclable trash sorting. It makes a positive contribution to environmental protection and sustainable development.

OPTIMIZING ENERGY EFFICIENCY: A COMPREHENSIVE ANALYSIS OF BUILDING DESIGN PARAMETERS

This study explores the critical role of financial incentives, energy performance certifications, government policies, and advanced technologies in driving the adoption of energy-efficient building practices. Financial incentives, such as tax credits, rebates, and grants, are shown to significantly reduce the upfront costs associated with energy-efficient technologies, making these innovations more accessible to developers and building owners. The research highlights the effectiveness of energy performance certifications, like LEED and Energy Star, in enhancing the market value and energy performance of buildings by providing a structured framework for evaluating sustainability. Government policies and building codes, including the International Energy Conservation Code (IECC) and ASHRAE standards, are identified as key drivers of energy efficiency, compelling developers to meet stringent performance standards. Additionally, the integration of renewable energy technologies, such as solar panels and geothermal heat pumps, alongside smart building automation systems, plays a vital role in reducing energy consumption and improving operational efficiency. However, regional disparities in the availability of financial incentives and enforcement of policies present challenges to widespread adoption. The study concludes that expanding financial support, strengthening policy enforcement, and increasing public and industry awareness of the long-term financial and environmental benefits of energy-efficient buildings are essential for accelerating the transition to sustainable building practices. By addressing these challenges, the construction industry can make significant strides toward reducing energy consumption, lowering greenhouse gas emissions, and achieving global sustainability goals.

Microencapsulated phase change material in 3D-printable mortars

The present study investigates the potential of replacing sand with microencapsulated phase change materials (MEPCM) in 3D-printable mortar to provide a promising way to improve thermal performance in 3D-printed buildings. Adding MEPCM significantly enhanced the rheological properties and early hardening evolution of cementitious mortar for 3D printing applications without the need for viscosity modifier agents. In hardened mortars, microstructural analysis and thermal cycling experiments confirmed that MEPCM remained intact and stable within the cementitious environment. The thermal properties of the treated mortars, including latent heat and thermal conductivity, were improved for energy-saving applications. Despite this, the compressive strength of the mortars dropped considerably by increasing the concentration of MEPCM while a strength of above 20 MPa was maintained. Simulation results from 3D Finite Element Method (FEM) and 1D reduced order model (ROM) closely matched the experimental data from printed walls in a thermal setup, validating the use of 1D ROM simulations for long-term predictions. In a case study, a printed wall where MEPCM replaced 80 % of the sand showed a ∼40 % reduction in energy consumption compared to mortar without MEPCM under real weather conditions.

Evaluation of transition to 100% electric vehicles (EVs) by 2052 in the United States

With the rising need to transition from fossil fuel consumption to renewables, the transportation industry is foreseeing large-scale adoption of electric vehicles (EVs). According to various studies, the petroleum resources will last until or around 2052. Utilizing the US Federal Highway Administration (FHWA) published data on the number of registered vehicles by each state, the profile of vehicles by 2052 was forecasted using a time series. The vehicle profile comprised vehicles by each type classified as automobiles, buses, trucks, and motorcycles. This future profile became the basis of the quantification of total EVs by the end of 2052. Based on the available fuel economy data published by the US Department of Energy, the average energy consumption per mile (kWh/mile) of each vehicle type was factored into the energy demand calculation for 100% electrification by 2052. A roof-top solar photovoltaic system is easy to install on unoccupied roof space for US house owners. Obtaining the capacity of such a roof-top solar PV system acts as a good decision-making criterion for both house owners and developers. However, for city-dwellers living in multi-family residential buildings, the potential of utilization of roof-top solar PV becomes a subject of future work when effective sharing of resources for local power generation by renewables and development of community-shared EV charging infrastructure is concerned. How well EVs performed against internal combustion engine (ICE) vehicles in terms of fuel efficiency and reduced carbon emissions is a driving factor for this transition. For energy demand calculations, a base model was framed, and results were obtained by utilizing mathematical and statistical tools. After accounting for the effects of improved fuel efficiency or reduced energy consumption by EVs over time, the model was modified to obtain revised energy demand and, thus, effective energy generation required. Accordingly, the capacity of EV charging infrastructure required by each state determined the need for preparation by utility companies and local jurisdictions. With limitations on battery size and thus increased frequency for drivers to return for charging, additional fast chargers (level 3) at existing gasoline stations become an option that requires further assessment.

Significantly enhanced energy efficiency through reflective materials integration in plant factories with artificial light

Plant factory with artificial light (PFAL) based on vertical farming is a promising technology for resource-efficient plant production, especially in urban areas or arid regions. However, the lighting system of PFAL requires a large amount of energy, which makes the total energy cost much higher than open-field and greenhouse farming. To address this issue, in this work, a cultivation system integrated with highly reflective materials is demonstrated in PFAL, which reduces light energy consumption by more than 36 %. A low-cost reflective film is fabricated with an overall reflectivity of 96.5 % in the entire photosynthetically active radiation spectrum. The effectiveness of this reflective film in reducing energy consumption is further demonstrated with optical simulation analysis and the corresponding computational fluid dynamics simulation based on an experimental cultivation rack. The corresponding field experiments of 3 different types of lettuce, Crunchy, Butterhead and Grand Rapids, under different light intensities are conducted in the cultivation unit. With reflective film properly installed, a 36 % reduction of light energy consumption is demonstrated while the dry weight and fresh weight still increase. An additional benefit is that the light reception area of lettuce becomes larger due to the more uniform lighting with the integration of reflective material. This work presents a simple yet reliable strategy for enhancing the energy efficiency in PFAL systems and can achieve the cost-effective daily production of lettuce with significantly reduced energy input.

Do China's ecological civilization advance demonstration zones inhibit fisheries' carbon emission intensity? A quasi-natural experiment using double machine learning and spatial difference-in-differences

China's National Ecological Civilization Demonstration Zone (NECDZ) policy has a significant role in ensuring national ecological security, and it is essential to investigate how the NECDZ policy affects the carbon emissions intensity of fisheries (CEIF) to advance China's commitment to reducing carbon emissions. This study evaluates the CEIF in 30 Chinese provinces from 2007 to 2021 using ecological civilization demonstration areas as a quasi-natural experiment and double machine learning (DML)to examine the impact and internal mechanisms of NECDZ implementation on the CEIF. We also explore spatial spillover effects using a spatial difference-in-differences approach. The results reveal that NECDZ implementation has a significantly negative impact on the CEIF and this effect continues over time. NECDZ policy potentially affects the CEIF through technology development, industrial structure improvement, and reduced energy consumption. Further investigation reveals that NECDZ implementation has spillover effects and inhibits the CEIF in surrounding regions. Therefore, it is essential to focus on developing the NECDZ policy to enhance fisheries' industrial structure, encourage low-carbon innovation in fishery technologies, and increase energy consumption efficiency. This could be supported by facilitating exchanges and cooperation with other areas, considering regional disparities, and assigning common but distinct responsibility for reducing the CEIF.

Optimizing educational environments: microclimate analysis and energy efficiency through courtyard orientation in UAE schools

Sustainable school design is becoming increasingly important worldwide, particularly in the UAE, where schools are significant energy consumers. This study explores the impact of courtyard orientation on microclimate and energy consumption in UAE schools, utilizing a standardized template applied across 70 existing schools. By employing advanced simulation tools, ENVI-met and IES-ve software, the research provides a comprehensive analysis of air temperature and energy use related to different courtyard orientations, specifically on key dates of September 21st and March 21st, representing seasonal variations. The results indicate that North-facing courtyards consistently provide cooler microclimates compared to other orientations. Specifically, North-facing courtyards showed temperature reductions of 1.31°C in September and 1.9°C in March compared to the least favorable orientations. This orientation recorded the lowest average mass temperatures of 29.36°C in September and 25.13°C in March, surpassing the West-facing orientation by 0.39°C and 0.45°C, respectively. The primary factor for this improvement is the reduced solar radiation exposure on East-West aligned courtyards, which significantly lowers the heat gain. Additionally, the study assessed Physiologically Equivalent Temperature (PET) readings and cooling demands, both of which were found to be lower in North-facing courtyards. Cooling load reductions varied between 1% and 4%, depending on the day, further emphasizing the efficiency of this orientation. These findings suggest that strategic courtyard orientation is a critical design consideration for enhancing thermal comfort and energy efficiency in school buildings. The implications of this research are significant for sustainable design and construction practices. By highlighting the benefits of optimal courtyard orientation, this study offers practical solutions for reducing energy consumption and improving the indoor and outdoor thermal environments of schools. These insights contribute to the broader goal of developing greener, more sustainable educational facilities, particularly in hot climates like the UAE. This research not only informs architects and urban planners but also supports policymakers in implementing effective sustainability strategies in the educational sector.

Thermal management of water electrolysis using membrane distillation to produce pure water for hydrogen production

When electrolysis units are used for water splitting to provide green hydrogen, waste heat must be dissipated to avoid performance degradation. Therefore, the hypothesis was that if a membrane distillation unit could use the waste heat to desalinate the feed water, then the operating costs may be reduced. This study demonstrated the first integration of a 0.03 m2 active area membrane distillation heat exchanger (MDHX) system with a 2.2 kW commercial electrolyser. The MDHX system satisfied the thermal requirements of the electrolysis unit. It was estimated that for a 2GW electrolysis plant, it could result in annual savings of up to $2 million. During operation with the 2.2 kW electrolyser, the MDHX unit was able to produce 50 % of the permeate rate required to match the water consumption of the electrolyser. Experimentally the MDHX system operated with a specific electrical energy consumption of 230 kWh/m3. Design changes such as improving pump efficiency and optimising flow field geometry to allow for lower flow rates, suggest there is scope for reduction in energy consumption. Indeed, further development of the MDHX technology is necessary before it can compete with large-scale seawater reverse osmosis (SWRO) methods.

Towards a sustainable decision analysis approach for moisture-safe building envelope design

Energy-efficient building designs, notably through adding insulation, can reduce energy consumption and greenhouse gas emissions by over 40 %, but may also increase risk of condensation. Therefore, a comprehensive decision analysis approach is essential for selecting optimal building envelope assemblies while considering their hygrothermal, economic, and environmental implications. This study introduces a novel decision analysis method based on the inutility decision analysis (IDA) concept, in which the building envelope with the lowest inutility is considered the optimal choice. Initially, different building envelope assemblies were assessed through probabilistic hygrothermal analysis combined with a machine learning metamodel. Subsequently, the environmental and economic consequences associated with each assembly were quantified by integrating moisture-related damage into these assessments. Combining these outputs yielded inutility indices for the case studies, guiding the selection of the most optimal alternative. The results indicated that an external wall, with a minor risk of moisture-related failure, may outperform other alternatives when considering economic and environmental metrics. However, the final decision on the acceptability of such risk should rest with the customers or owners. Balancing the trade-off between performance longevity, cost, and occupants’ health is crucial.

Innovative simplified approach and numerical investigation for flow and heat transfer in internally threaded tubes

Internally threaded tubes exhibit exceptional heat transfer capabilities, holding paramount significance in enhancing energy efficiency, reducing energy consumption, and tackling thermal challenges across aerospace, automotive, and environmental domains. Nonetheless, their intricate structural features, software modelling intricacies, extensive grid requirements, and challenges in maintaining grid quality render numerical simulation investigations a demanding endeavor. This study elucidates the influence mechanisms of thread spacing on the distribution of single-phase and two-phase flows. It delves into the condensation heat transfer of the R32 refrigerant and the single-phase heat transfer of water within internally threaded tubes. At the same Reynolds number, the heat transfer rate can be improved by approximately 19.61% with the optimized internally threaded parameters. When the Reynolds number varies from 18,626 to 51,222, the heat transfer rate per unit length for Tube-5 remains around 2,478.9 W/m, with a variation of less than 5.19%. Furthermore, it presents a simplified numerical simulation approach tailored for internally threaded pipes. This method manipulates wall roughness and regulates wall rotation speed to mimic fluid dynamics phenomena akin to conventional numerical simulation techniques. The findings reveal that the proposed simplified numerical simulation approach yields heat transfer and pressure drop predictions with less than 3% discrepancies compared to traditional numerical simulation predictions. Simultaneously, compared to directly simulating threaded pipes through conventional means, this approach significantly mitigates grid generation complexities, slashing computational time costs by at least 85% and facilitating efficient thermal design and structural optimization processes.

Lightweighting cost impacts on market adoption and GHG emissions in U.S. light-duty vehicle fleet

Abstract Vehicle lightweighting is a promising strategy that can reduce energy consumption and GHG emissions without compromising vehicle’s performance or size. The cost of lightweighting plays a critical role in determining the adoption of lightweighting technologies by consumers and manufacturers among advanced vehicle technologies. This analysis estimates the cost of lightweighting needed to achieve significant light-duty vehicle adoption to provide reductions in GHG emissions. Three different costs of lightweighting scenarios in the U.S. market including a baseline scenario, advanced technology scenario, and widespread scenario are evaluated employing Automotive Deployment Options Projection Tool (ADOPT). ADOPT leverages a database of over 700 existing vehicle models and options, enabling it to provide a high degree of realism and capture the unique characteristics of popular vehicles and the endogenously evolvement of the vehicle options. For baseline scenario, the lightweighting fraction reached by about 15% and GHG emissions are reduced by more than 50% in 2046 compared to 2015. The widespread scenario further reduces the GHG emissions by about 4% from the additional 10% glider mass reduction compared to the baseline scenario. The benefit came largely from lightweighting being implemented in the large market segment of lower-price vehicles, due to the relatively low lightweighting cost ($5/kg).

A-204 Commitment to Sustainability From Laboratory Medicine

Abstract Background According to McGill University, sustainability means “meeting our needs without compromising the ability of future generations to meet their own needs”. Sustainable environmental practices improve water and air quality, reduce landfills, and increase renewable energy sources in the long term. In response to the growing urgency of global sustainability challenges, the healthcare industry is taking a step forward to reduce its environmental impact. University of Malaya Medical Centre (UMMC) is a government-funded teaching hospital and medical institution, which processes about 3.95 million chemistry and 371,000 immunoassay tests per year. This study aims to evaluate the sustainability aspects of Atellica Solution, focusing on water usage, power consumption, carbon dioxide emissions and plastic waste generations. Methods Power and water consumption for the analysers Advia Chemistry 2400 & XPT and Advia Centaur XPT were obtained from Atellica Solution using the year 2022 test volumes and compared with those of Siemens legacy system for the year 2019 test volumes. In addition, the reduction of plastic usage was also assessed for the year 2019 and 2022. Results With innovative Atellica Solution replacing Siemens legacy system, the data showed reduction in water and energy consumption by 36.44% and 47.74% respectively, whereas the annual throughput increased by 13.7%. By using Atellica INTELIQ QC management, fewer aliquot tubes were used and thus plastic waste was reduced by 168 kg. These reductions combined to lower CO2 emissions by about 16 tons. Conclusions Atellica Solution is designed for high throughput and sustainability, reducing water and energy consumption while also significantly reducing CO2 emissions. Furthermore, it enables faster, streamlined quality control management with Atellica INTELIQ QC management system. UMMC is strongly committed to minimizing laboratories’ environmental footprint and provide better access to care eventually contributing to the well-being of patients.

Method of cooling server rooms

Objective. In today's world, where the amount of data is growing every day, the importance of reliable and efficient data centers (DPCs) is becoming increasingly clear. One of the key aspects of maintaining data center availability and reliability is ensuring adequate cooling of server rooms. This scientific work is devoted to the study and analysis of various cooling techniques used in server rooms. Method. The study is based on thermodynamic analysis methods to optimize cooling systems to improve their efficiency and reduce energy consumption. Result. The work examines both traditional cooling systems and modern innovative solutions that improve cooling efficiency while reducing energy consumption. The authors compare different cooling methods, evaluate their advantages and disadvantages, and analyze the potential economic and environmental benefits of introducing advanced technologies into server room management practices. Conclusion. The choice of the optimal cooling method should take into account the specifics of the server equipment, thermal loads, room size and other factors. It is also important to pay attention to maintenance and selection of the optimal cooling system in accordance with the needs and characteristics of the server equipment.

Artificial Intelligence-Driven Regional Energy Transition:Evidence from China

As Artificial Intelligence (AI) technology advances rapidly, its role in promoting regional energy transformation is becoming increasingly apparent. This paper utilizes regional-level panel data from China, covering the period from 2011 to 2021, to systematically assess the impact of AI development on energy transformation. Using econometric models, including fixed-effects and instrumental variable regressions, the study reveals that an increase in AI enterprises within a region significantly reduces energy consumption per unit of GDP, thereby accelerating regional energy transition. The analysis identifies two primary channels through which AI exerts this effect: by facilitating the upgrading of the regional industrial structure and by promoting the growth of the digital economy. The findings also show that the impact of AI is more pronounced in highly urbanized regions, particularly in the Yangtze River Economic Belt. Additionally, the results highlight that AI-driven reductions in energy consumption are largely achieved through improved efficiency in coal and electricity usage, addressing key structural issues in China's energy landscape.

Particle Swarm Optimization for multi-chiller system: Capacity configuration and load distribution

This study applies Particle Swarm Optimization (PSO) to enhance the energy efficiency of a multi-chiller system in a large office building, with a focus on optimizing capacity configuration and load distribution. Given the rising demand for sustainable energy solutions in buildings, where HVAC systems, particularly chillers, account for a significant portion of energy consumption, this research aims to reduce energy use and improve system performance. EnergyPlus simulations, based on Baltimore weather data and a reference large office model, were conducted to build a baseline dataset. This dataset was then used in Python to calculate energy consumption and optimize load distribution and capacity configuration. PSO was applied in three stages: optimizing capacity configuration using five built-in EnergyPlus algorithms, optimizing load distribution, and simultaneously optimizing both. The results showed that optimizing capacity configuration alone improved performance, even using traditional load distribution methods. Load distribution optimization outperformed other algorithms and converged at a 0.7 Part Load Ratio (PLR) for staging. The integrated PSO application achieved an 11.3 % reduction in energy consumption, a 21.5 % improvement in Coefficient of Performance (COP), and a 12.8 % increase in the Seasonal Energy Efficiency Ratio (SEER) by precise operation of 15 different combinations throughout the entire load range. These results demonstrate the potential of PSO to significantly enhance the efficiency of multi-chiller systems, providing a novel approach to optimizing both capacity configuration and load distribution. This research contributes to a robust framework for improving energy performance in building systems and offers valuable insights for future sustainable energy solutions.

Rapid quantitative analysis of three elements (Al, Mg and Fe) in molten zinc based on laser-induced breakdown spectroscopy combined with machine learning algorithm

Hot-dip galvanizing represents one of the most cost-effective methods for the prevention of metal corrosion, and is therefore employed extensively across a range of fields. Carrying out the research and development of technology and devices for quantitative analysis of chemical elements in hot-dip galvanising process can provide theoretical basis and technical support for the efficiency of hot-dip galvanising process and reduction of energy consumption. A machine learning-assisted LIBS combined with a programmable logic controller (PLC) for simultaneous on-line/in-site monitoring of multiple elements in hot-dip galvanising solution (molten zinc) was developed in the current study. The LIBS spectral data of the on-site hot-dip galvanising solution was collected under optimised experimental conditions. In order to further reduce the influence of experimental noise on the analysis performance, the on-site LIBS spectral data were preprocessed and anomalous spectral data were screened based on normalisation and principal component analysis-mahalanobis distance (PCA-MD). On the basis of the optimised data, component prediction models for three key elements of on-site hot-dip galvanising solution were constructed. The storage and re-call of the model was achieved based on python combined with LabVIEW, thus real-time prediction of the on-site component content of hot-dip galvanising solution was achieved. The results show that the random forest model presents the best prediction results, in which the R2 is 0.9978 and the RMSE is 0.0013% for Al, the R2 is 0.9984 and the RMSE is 0.0011% for Mg, and the R2 is 0.9932 and the RMSE is 0.0001% for Fe. From the on-site analysis results of the constructed model, its MRE of Al, Mg and Fe is 0.0098, 0.0236, and 0.2102, respectively. In summary, the in-situ/on-line analysis system of hot-dip galvanising solution combined with machine learning constructed in this study shows excellent performance, which can satisfy the needs of hot-dip galvanising solution production site. This study is expected to provide theoretical basis and technical reference for quality control and process optimisation in other production sites in the metallurgical field.

Fast biodiesel production via potassium ferrate catalysis at room temperature using used cooking oil and refined canola oil

Abstract As biodiesel is continuously aimed to be a better alternative to petroleum-based diesel fuel, the University of the Philippines Los Baños – Interdisciplinary Biofuels Research Studies Center (UPLB-IBRSC) continues to explore process conversion techniques to improve efficiencies in biodiesel production. In this study, the catalytic activity of potassium ferrate in the transesterification of canola oil and used cooking oil for biodiesel production at room temperature was examined for potential reduction in energy consumption. Parameters such as methanol-to-oil molar ratio, catalyst loading, and reaction time were varied to determine their effects on biodiesel yield and purity. Thin layer chromatography was applied to qualitatively analyze the biodiesel, while ImageJ software was used for purity determination. The characterization analysis confirmed that the refined canola oil used in the study has an acceptable FFA content of 0.06%. On the other hand, the used cooking oil showed an FFA content of 3.71%, which is beyond the desired limit of 2%. Hence, neutralization through the addition of potassium hydroxide was performed, resulting in a lowered FFA content of 0.86%. Experimental results of the parametric and optimization study on used cooking oil showed that an optimum conversion of 86.55% was achieved at a 2:1 methanol-to-oil molar ratio, 5 wt% catalyst loading, and 90 minutes reaction time. Consequently, a yield of 83.21% was attained using refined canola oil at the optimum conditions of 6:1 methanol-to-oil molar ratio, 3 wt% catalyst loading, and 69 minutes reaction time. Generally, findings show that catalyst loading and reaction time significantly affect the biodiesel yield and purity only up to a certain optimum value. The methanol-to-oil ratio, on the other hand, showed to have a significant inverse relationship on yield and purity. Compared to the typical biodiesel production process at 60 °C with up to two hours of reaction time, this study which describes a catalytic process at room temperature and a relatively shorter reaction time, showed the potential to reduce significantly the high energy consumption in biodiesel production.