Total Demand Research Articles

Optimizing the Operation of an Electrolyzer with Hydrogen Storage Using Two Different Methods: A Trade-Off Between Simplicity and Precision in Minimizing Hydrogen Production Costs Using Day-Ahead Market Prices

The potential for hydrogen is high in industrial processes that are difficult to electrify. Many companies are asking themselves at what cost they can produce hydrogen using water electrolysis with hydrogen storage. This article presents a user-friendly and less computationally intensive method (called method 1 in the following) for determining the minimum of the levelized cost of hydrogen (LCOH) by optimizing the combination of electrolyzer size and hydrogen storage size and their operation, depending on electricity prices on the day-ahead market. Method 1 is validated by comparing it with a more accurate and complex method (called method 2 in the following). The methods are applied to the example of a medium-sized industrial company in the mechanical engineering sector with a total natural gas demand of 8 GWh per year. The optimized LCOH of the analyzed company in method 1 is 5.00 €/kg. This is only slightly higher than in method 2 (4.97 €/kg). The article shows that a very good estimate of the LCOH can be made with the user-friendly and less computationally intensive method 1. For further validation of the methods, they were applied to other companies and the results are presented below.

Decomposition analysis of renewable energy demand and coupling effect between renewable energy and energy demand: evidence from China

Driving up demand for renewable energy is key to China's response to the energy crisis and to controlling carbon emissions. Several studies have been conducted to explore the factors affecting renewable energy demand. However, the literature on the importance of technological innovation in fossil energy production in affecting renewable energy demand is scant. Therefore, it is essential to explore the impact of technological innovations in fossil energy production on renewable energy demand, as well as to reveal the dynamic penetration mechanism of renewable energy in the field of energy demand, to provide policy guidelines for China to promote its energy revolution. This study decomposes renewable energy demand into 10 drivers with the extended IDA-PDA (Index Decomposition Analysis and Production-theory Decomposition Analysis) model. Then the dynamic penetration mechanism of renewable energy on the energy demand side is revealed by combining the coupled model and the decomposition results. The results show that the investment intensity effects and energy restructuring contributed the most to renewable energy demand growth, contributing 60.73% and 79.97% respectively. Conversely, technological advances in fossil energy combustion made the largest negative cumulative contribution to renewable energy demand growth, with a cumulative negative contribution of -39.57%. The drivers of renewable energy demand vary considerably across types, regions and resource endowment levels, which means that regions should develop differentiated development strategies based on their own characteristics. In addition, the coupling effect between renewable energy demand and total energy demand is weakening, with market and policy effects becoming the drivers for augmenting the response of renewable energy to total energy demand. The findings of this study provide clear pathways for expanding renewable energy demand, offering important insights for the government to balance between promoting capital-driven interests and policy-driven development.

Breakdown cost and recycling processes of Bentonite/TiO2 quantum dots of photo and solar degradation of Congo Red dye and industrial dyes wastes

This research presents the preparation, characterization, and application of a novel bentonite- TiO2 quantum dot (Bent/TQ) nanocomposite for the efficient photodegradation of Congo Red dye, a common pollutant in industrial wastewater. The composite was prepared via a co-precipitation method followed by calcination, which facilitated the uniform dispersion of TiO2 quantum dots within the bentonite matrix. The resulting material exhibited a high specific surface area of 205.45 m2/g and an optimized band gap of 3.15 eV, making it suitable for visible light photocatalysis. The photocatalytic efficiency of Bent/TQ was evaluated under both xenon lamp irradiation and natural sunlight. The composite demonstrated a significant degradation rate constant of 23.35 × 10⁻³ s⁻1, which is comparable to that of pure TiO2 quantum dots (28.86 × 10⁻³ s⁻1). Mechanistic studies revealed that the primary active species responsible for the degradation were hydroxyl radicals (•OH), generated through the interaction of photogenerated electron-hole pairs with H2O molecules and molecular oxygen. Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD) analyses were employed to assess the mineralization efficiency of the composite. The Bent/TQ catalyst achieved a COD removal efficiency of 84.5% and a TOC reduction of 76.2% after 240 minutes of irradiation under natural sunlight. Reusability tests indicated that the photocatalyst retained approximately 80% of its original efficiency after seven consecutive cycles, though a slight decline was observed due to particle agglomeration under prolonged light exposure. The economic analysis suggested that the use of Bent/TQ nanocomposites could reduce operational costs by 29.12% compared to conventional TiO2-based systems, making it a cost-effective solution for large-scale wastewater treatment. This work highlights the potential of Bent/TQ nanocomposites as sustainable and efficient photocatalysts for environmental remediation, with future efforts aimed at enhancing their long-term stability and expanding their applicability to a broader range of pollutants.

Green Energy Transition: Evaluating Carbon-To-Liquid Technologies for 2050 Climate Goals and Energy Security

China's coal-to-liquid (CTL) sectors must quickly adjust to a low-carbon economy to meet the energy demand while mitigating environmental and energy security challenges. Similarly, China's climate goals for 2050 are gravely jeopardized if the industry's enormous ecological impact is not addressed. This research develops a conceptual framework for environment-security-economic evaluation of the high-carbon energy sector. Moreover, it constructs a system dynamics material model to predict the upgrade plan's industrial capacity and carbon emission scale. Finally, it calculates the dynamic optimal deployment scale at various epochs based on the sequential decomposition goal of "carbon peak" and "carbon neutrality." These measures are needed to anticipate the development of China's CTL industry. The road map predicts that by 2025, China's CTL industry will be able to meet at least 5% of the country's total oil demand. The adoption of scale A process will improve the economic and environmental performance of the CTL industry due to the cost savings and efficiency gains at a larger scale. In addition, the industry's overall energy efficiency will rise in 2023 and exceed 50% after 2035. Findings indicate that the CTL sector will approach a carbon peak in 2029 and neutrality and transition to hydrogen generation by 2050.

A Stochastic Approach to the Power Requirements of the Electric Vehicle Charging Infrastructure: The Case of Spain

Battery electric vehicles represent a technological pathway for reducing carbon emissions in personal road transport. However, for the widespread adoption of this type of vehicle, the user experience should be similar to that of combustion engine vehicles. To achieve this objective, a robust and reliable public charging infrastructure is essential. In Spain, the electric recharging infrastructure is growing quickly in metropolitan areas but much more slowly on roads and highways. The upcoming charging stations must be located along high-volume traffic corridors and in proximity to the Trans-European Transport Network. The main contribution of this research is to offer a method for examining the essential electricity infrastructure investments required in scenarios involving substantial electric vehicle adoption. The methodology includes a sensitivity analysis of fleet composition and market share, recharging user behavior, charging station density, and vehicle efficiency improvements. To this end, the authors have developed a simplified probabilistic model, addressing the effect of the involved parameters through a comprehensive scenario analysis. The results show that the actual number of high-capacity charging plugs on Spanish roads is significantly lower than the European regulation requirements for the year 2030 considering an electric vehicle market share according to the Spanish Integrated National Energy and Climate Plan 2021–2030 objectives and it is far from the necessary infrastructure to cover the expected demand according to the traffic flow. Under these circumstances, the charging peak power demand reaches over 7.4% of the current Spanish total power demand for an electric vehicle fleet, which corresponds to only 12% of the total.

Forecasting Wholesale Electricity Market Prices Considering Bidding Conditions Using Price Sensitivity

This article proposes a sophisticated forecasting model to predict significant price volatility on the Japan Electric Power Exchange (JEPX) due to the growing influence of solar photovoltaics (PV) in the energy mix. As solar power generation continues to grow, it significantly impacts the daily and seasonal price trends in the market. This method employs a neural network that integrates daily prices, net electricity demand (total demand minus PV output), and underlying time‐series data to determine the objective variable, defined as the deviation from the average price over the past week. Incorporating price sensitivity data from JEPX increases the accuracy of the model. These data reflect how prices respond to market‐specified bid adjustments, including a variety of bidding, providing a nuanced understanding of market responses. The results demonstrate that adding multiple price sensitivities significantly improves the model's ability to detect price spikes and provides a robust tool for transmission and distribution system operators to manage risk and optimize their market strategies in an increasingly renewable energy‐dominated landscape. This approach not only addresses daily and seasonal price fluctuations but also aligns with broader sustainability goals as the share of solar PV generation continues to grow.

The Impact of Trade Openness on the Industry Reality in Iraq for the Period (2004-2022)

Iraq is one of the prominent countries that used to have important industries and enjoyed consumer acceptance, competitiveness, and presence in regional and global markets. However, after 2003 and due to trade liberalization, there needed to be more in the structure of the Iraqi economy. This directly affected the reality of the Iraqi industrial sector, leading to the entry of goods and products from all over the world. With the increase in population and government expenditures, which relied on commodity imports to meet local needs, all aspects of industry in the country were eliminated. The importance of research lies in shedding light on the role of international trade (import and export) in global economies, highlighting the shortcomings in trade policy management, demonstrating the impact of trade openness on the industrial sector's contribution to economic development, and the need for realistic measures to rescue this vital sector from deterioration in Iraq. Moreover, the research problem is based on the direct negative impact of trade liberalization policy on the reality of the industrial sector in the country. Increased reliance on foreign sources to meet local economic needs is attributed to the production apparatus's deficiencies, backwardness, and stagnation, rendering it unable to meet total domestic demand. The research aims to shed light on the role of foreign trade in global economies and to clarify the shortcomings in trade policy management, as well as the extent of the impact of trade liberalization on the reality of the industrial sector in achieving economic development. The research found that the industrial sector in Iraq was negatively affected throughout the period (2004-2022) due to the compulsory trade liberalization policy, which led to the shutdown of factories and plants because of the low prices of imported goods. Paper type Research paper.

Maize disease classification using transfer learning and convolutional neural network with weighted loss

Maize stands out as a versatile commodity, finding applications in food and animal feed industries. Notably, half of the total demand for maize is met through its utilization as animal feed. Despite its importance, maize cultivation often grapples with crop failures resulting from delayed disease management or insufficient knowledge about these diseases, impeding timely intervention. The advent of technological advancements, particularly in Machine Learning, presents solutions to address these challenges. This research focuses on employing a Convolutional Neural Network (CNN) to classify maize plant diseases. Two datasets form the foundation of this study. The first dataset encompasses 4144 images distributed across 4 classes, while the second dataset comprises 5155 images distributed among 7 to 8 classes. The second dataset encounters issues related to imbalanced class distribution, where certain classes possess substantially more data than others. To mitigate this imbalance, the weighted cross-entropy loss method is employed. During experimentation, three distinct architectural models—ResNet-18, VGG16, and EfficientNet—are rigorously tested. Additionally, various optimizers are explored, with noteworthy results indicating that both datasets achieve peak accuracy through the use of the SGD (Stochastic Gradient Descent) optimization. For the first dataset, optimal results are obtained with the VGG16 architecture, leveraging a frozen layer in the classification stage and achieving an impressive accuracy of 97.146 %. Shifting the focus to the second dataset, the most favorable outcome is realized by employing the EfficientNet architecture without a frozen layer, coupled with the implementation of weighted loss to address the class imbalance, resulting in an accuracy of 94.798 %.

Classification and Evaluation of Marginal Land for Potential Cultivation in Northwest China Based on Contiguity and Restrictive Factors

Water, land, and other environmental conditions restrict marginal land (ML) conversion into newly cultivated land. Accurately evaluating ML’s development and utilization potential (DUP) can provide critical support for increasing new cultivated land and ensuring food security. This study focuses on Northwest China, using spatial identification of different types of ML based on remote sensing images, and constructs a county-level DUP evaluation model through contiguous characteristics and restrictive factors to determine new cultivated-land potential, water demand, and liftable grain production. The results show that the DUP of ML in Northwest China is 12.59 million ha, with low-efficiency cultivated land (LCL) and two types of restoration land (TTRL) accounting for 3.29% and 5.95%, and other marginal land (OML) making up 90.76%. The total water demand for ML development and utilization is 69.87 billion cubic meters, which can increase grain production by 62.31 million tons. The coordinated development of water, land, and food promotes an increase in grain production, with water resources being the main restrictive factor. This model effectively evaluates DUP and provides a scientific basis for promoting the rational use of water and land resources. Further research should set up more detailed water resource utilization strategies and scenarios as well as find more development and utilization techniques.

The Impact of Trade Openness on the Reality Industry in Iraq for the Period (2004-2022)

Iraq is one of the prominent countries that used to have important industries and enjoyed consumer acceptance, as well as competitiveness and presence in regional and global markets. However, after 2003 and due to trade liberalization, there was a general weakness in the structure of the Iraqi economy. This directly affected the reality of the Iraqi industrial sector, leading to the entry of goods and products from all over the world. With the increase in population and government expenditures, which relied on commodity imports to meet local needs, all aspects of industry in the country were eliminated. The importance of research lies in shedding light on the role of international trade (import and export) in global economies, highlighting the shortcomings in trade policy management, demonstrating the impact of trade openness on the industrial sector's contribution to economic development, and the need for realistic measures to rescue this vital sector from deterioration in Iraq. And the problem of the research is based on the direct negative impact of trade liberalization policy on the reality of the industrial sector in the country. Increased reliance on foreign sources to meet local economic needs is attributed to the deficiencies, backwardness, and stagnation of the production apparatus, rendering it unable to meet total domestic demand. The aim of the research is to shed light on the role of foreign trade in global economies and to clarify the shortcomings in trade policy management, as well as the extent of the impact of trade liberalization on the reality of the industrial sector in achieving economic development. The research found that the industrial sector in Iraq was negatively affected throughout the period (2004-2022) due to the compulsory trade liberalization policy, which led to the shutdown of factories and plants as a result of the low prices of imported goods. Paper type Research paper.

Design of a positive energy district: A Nigerian case study

The potential of possible solutions for the design and implementation of a 50-house Positive Energy District (PED) in Southern Nigeria is presented in this study. Using a transient energy systems simulation software (TRNSYS 18), the energy demand of a typical building is estimated. Different passive design options considering building materials, window sizes, building orientation, roof ventilation are studied to determine the most energy efficient building envelope. TRNSYS and PVSyst (another energy simulation software), are also used to simulate the dynamics of a positive energy building and operation of solar energy systems used for electricity generation, domestic hot water heating and space cooling for Lagos weather conditions. In terms of electric energy balance, the total annual electricity demand is 598,000 kWh while the electricity generated by PV is 728,600 kWh in a year. As regards to site energy balance, annually, the district exports more electricity than it imports. The difference is 110,200 kWh. These results show that in such climatic conditions, despite economic poverty, it is possible to implement the modern PED principles. However, first, it is necessary to reduce energy consumption as much as possible, including solar passive solutions to the building architecture.

A perspective on solar railway potential for the UK

AbstractIn the face of climate change challenges, energy demand is still increasing, and renewable energy is becoming increasingly necessary as a sustainable resource. Solar energy stands out as a promising renewable resource, yet its widespread adoption faces challenges, notably the substantial land requirements for photovoltaic (PV) panels. This conflict intensifies as land could be more efficiently utilized for agriculture and development. Conversely, railway infrastructure occupies expansive tracts of land, presenting an opportunity to integrate PV panels without disrupting rail operations. However, a comprehensive assessment of the solar potential along railway tracks on a national scale is lacking for different countries and the UK is one of them. This study addresses this gap by evaluating the feasibility of installing PV panels on railway tracks throughout the UK to generate electricity. Utilizing mathematical models tailored to five distinct solar panel technologies, the study incorporates various factors, including solar radiation levels across different months. Assumptions were made where necessary, with values for certain variables averaged to facilitate calculations. The findings reveal that solar energy harnessed from railways could satisfy up to 8% of the UK's total electricity demand, with a minimum contribution of 0.3%. This study provides valuable insights from a UK‐centric perspective and offers a replicable framework for similar assessments in other countries with extensive railway networks, such as China and India. By leveraging existing infrastructure for renewable energy generation, such initiatives could contribute significantly to global sustainability efforts.

Re-envisioning par for the course: an assessment of desert golf sustainability from a landscape perspective

ABSTRACT Despite providing valuable ecosystem services (benefits people derive from nature), desert golf courses consume enormous amounts of water and can exacerbate water scarcity in drylands. Yet, few studies have assessed the sustainability of desert golf from a landscape sustainability perspective, which centers on the linkages among landscape pattern, ecosystem services, and human well-being. Focusing on the Phoenix Metropolitan Area (PMA) of USA, a global golf epicenter, this study investigated the relationships among golf course spatial pattern, water use, ecosystem services, and environmental justice. Our analysis revealed that there are 210 golf courses in the PMA, together making up roughly 3.5% of total regional water demand. Furthermore, we found that people from high-income brackets have greater access to the benefits provided by golf courses than those in low-income brackets. To improve desert golf sustainability, we need to design courses that use less water and provide a wider range of ecosystem services beyond the game of golf, including climate regulating services and cultural services. Golf courses should be considered explicitly in sustainable landscape design, with particular emphasis on the diversity and accessibility of ecosystem services they provide in a regional context.

Impact of space utilization and work time flexibility on energy performance of office buildings

This study investigates the impact of different space utilization on energy use intensity, heating load, cooling load, and thermal comfort of occupants using a combination of empirical data gathering and simulation-based studies. The increasing worldwide preoccupation with energy consumption and environmental sustainability has led to increased attention on optimizing interior spaces to mitigate total energy demand. The considered case study for conducting this research was the Norwegian living lab namely Zero Emission Building (ZEB) Flexible Lab in Trondheim, Norway. In this study, 10 different scenarios of occupancy schedules based on flexible arrangements from standard workweeks to extensive remote work configurations were designed and analyzed using IDA ICE 5.0. The findings demonstrate significant reductions in energy use across scenarios with increased teleworking and compressed work weeks. The remote scenario achieved the most significant decrease in Energy Use Intensity (EUI), with a reduction of 46 % compared to the base case. Similarly, the implementation of flexible hours and remote working in scenarios resulted in a reduction of electric heating demand by up to 23 %, underscoring the potential of occupancy-based strategies in enhancing building energy efficiency. However, uncomfortable hours increased by 59 % in the 2-day remote working scenario compared to the base case, demonstrating the need to consider climate conditions when implementing remote work. The research offers valuable insights into the complex connections between flexible arrangements and energy efficiency, considering many elements such as occupancy schedule and use dynamics. This article offers a comprehensive analysis that may give architects, building managers, and policymakers valuable insights. This study contributes to the developing sustainable architecture by emphasizing the impact of dynamic occupancy on the energy performance of office buildings.

Electric vehicle charging station demand prediction model deploying data slotting

Accurate prediction of energy requirement at charging station is essential for optimizing infrastructure usage, ensuring grid stability, and minimizing operational cost. Literatures suggest deployment of machine learning techniques to forecast the station demand. One major challenge associated with development of machine learning models is the inherent uncertainty in electric vehicle charging behaviour that includes variations in charging patterns, user preferences, and vehicle types. The conventional pre-processing techniques fail to dislodge nonlinearities and highly random patterns that include very low or zero-charging. Employing such techniques affects the model's forecast accuracy. This article performs data-slotting during pre-processing stage and then selects the best among 1-h, 2-h, 3-h and 4-h slots, to frame the feature vectors. The 4-h data with minimum variance is suggested to frame the dataset. Four distinct datasets, comprising different combination of average and total demands as predictor and response respectively are considered. The created dataset is deployed in Random Forest, Categorical Boosting, Extreme Gradient Boosting and Light Gradient Boosting models. The article recommends Categorical Boosting Regression model with least mean absolute error, mean square error and root mean square error of 0.0726, 0.0112, and 0.1059 respectively. Furthermore, the use of feature vector comprising of aggregated load for prescribed slots and the response representing the aggregated demand is observed to provide the least prediction error by the suggested model. The suggested model fed by the proposed feature vector offers significant advantage to charging station operator by enhancing the operational efficiency while performing resource and cost management with strategic planning.

Assessing Water Balance Influenced by Conservation Measures for Sustainable High-value Vegetable Cultivation: A Case Study of Battuvagu Watershed in Telangana, India

Rainfed areas are crucial for India’s agriculture, covering 50% of the total farmland and contributing 40% of the country’s food production. In recent years, the adverse effects of climate change have been found to be exacerbating the complex problems in vulnerable rainfed areas. While climate change affects many aspects of the environment, its impact on water resources is often swift and apparent, with far-reaching consequences for ecosystems, human well-being, and sustainable development. In this context, water budgeting at watershed level and its management are crucial towards promotion of climate resilient agriculture in the rainfed ecosystems. Comprehensive assessment of water balance was attempted, estimating the demand from agriculture, domestic and livestock needs and availability of water in terms of groundwater recharge from different sources and effective surface water stored in an agricultural watershed falling in Siddipet district of Telangana state of India. It involved collection of data on population, land use, and estimating irrigation water needs for crops, humans and livestock, followed by inventorying surface storage structures. Further, water inflow, runoff, and groundwater recharge were calculated using standard methods. The water balance was then determined by subtracting total demand from total availability. The study revealed that the water balance improved significantly, shifting from a deficit (-1.18 ha-m) of pre-project phase to a surplus (+67.91 ha-m) during post-watershed development. Thus, this study revealed the effectiveness of soil and water conservation measures taken up as a part of watershed development project in enhancing groundwater recharge and effective surface water storage capacity leading to positive water balance. The study also evaluated the use of groundwater in terms of stage of development in the pre and post project scenarios. The study concluded that there is a need for controlling the over extraction of groundwater through crop diversification especially towards market driven, less water consuming high value vegetable cultivation based on water availability with active people participation along with awareness building, propagation of efficient water application (micro-irrigation) methods, group mode of irrigation, and conjunctive use of surface and groundwater, etc. for long-term water security in the watershed.

A multi-objective approach to optimizing the geometry and envelope of rural dwellings for energy demand, thermal comfort, and daylight in cold regions of China: A case study of Shandong province

Rural dwellings in China’s cold regions face significant challenges related to energy consumption, thermal comfort, and daylight, with a projected substantial increase in energy demand in the future. Scientifically optimized design during the early planning stages can significantly enhance indoor thermal and daylight conditions, effectively reducing energy consumption. This study focuses on Shandong Province to comprehensively analyze the optimization of geometry and envelope design of rural dwellings in China’s cold regions. It employs a multi-objective optimization method, integrating genetic algorithms with dynamic energy simulations, to minimize total energy demand while ensuring thermal comfort and sufficient daylight. Utilizing the Octopus plugin in Grasshopper, in conjunction with EnergyPlus, Ladybug, and Honeybee, this research optimizes and analyzes the impact of various design variables on energy consumption, thermal comfort and daylight, including room depth, window-to-wall ratio in different orientations, thermal performance of the envelope, and related glass material parameters. The results indicate that the selected variables have significant energy-saving potential. Compared to the case study building, the optimized solutions can reduce total energy demand by over 62.41%, with the highest savings rate reaching 88.75%. This study emphasizes the importance of integrated design and optimization strategies in enhancing the sustainability of rural dwellings. It proposes both cost-free and cost-included optimization schemes: the former maximizes the energy-saving, thermal comfort and daylight potential of the selected variables, while the latter is more practically feasible considering the economic constraints of rural areas. The proposed optimization schemes provide multiple reference solutions and theoretical foundations for users of rural dwellings in China’s cold regions.

Discovery and Underground Mining of Large Deposits: Essential Training to Ensure Copper Supply

Editor’s note: The aim of the Geology and Mining series is to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. Abstract A supply shortfall of at least 6 million tonnes (Mt) Cu is expected by 2031—with total demand now forecast to increase from 25 Mt in 2023 to >36 Mt, and possibly to a suggested 50 Mt by 2050—given the present trend in demand. The deficit can be partially filled by increased mine production, expanding currently installed mine production capacity, and increasing Cu recycling. However, satisfying demand in 2050 as the demand-to-mining supply gap increases will require the accelerated discovery of at least five large Cu deposits—individually producing >0.5 to >1.0 Mt/yr Cu—and their related mine development, by 2030. These five deposit discoveries are required to replace mines scheduled to close in the 2050s, given the >20 years needed to take a deposit from discovery to mine production. The two largest sources of Cu supply, porphyry and sediment-hosted Cu deposits, can potentially fill the gap if enough discoveries are made; however, there is no evidence that this is about to occur. Future porphyry Cu discoveries are expected to be mined underground, mostly by a caving method. The demand increase is occurring at a time when the capital intensity of porphyry Cu mine development has almost doubled since 2006. We predict that the only solution to the future Cu demand-supply challenge for society and the mining industry in the short to medium term rests with simple demand-supply economics and a substantially increased Cu price. Copper substitution by other materials and recycling could potentially benefit from a significantly increased price; however, the major advantage will be conferred on deposits amenable to open-pit mining that are undeveloped because of price-dependent, failed feasibility studies. The challenge of possibly doubling Cu supply by ca. 2050 through developing mines on large Cu deposits—both discovered and undiscovered, in an unprecedented number and in a shorter period—will require a major increase in the number of competent mining industry personnel. This comes at a time when the industry faces an aging workforce, leading to a critical skills gap compounded by a chronic shortage of students graduating in the sciences and engineering with mining-related university degrees. There is an acute shortage of experienced professionals in cave mining, and the substantial number of open-pit mining professionals who are predicted to retire by 2030 will need to be replaced. On-the-job training of geoscientists and engineers after graduation, delivered by experienced practitioners through university-accredited professional development programs, is suggested as the solution.

طرق التعلم الآلي للتنبؤ بالأحمال متوسطة المدى: دراسة على محطات شمال بنغازي التابعة للشركة العامة للكهرباء في شرق ليبيا

The General Electricity Company of Libya (GECOL) has experienced a surge in electricity demand in recent years, leading to power shortages, particularly during peak summer months. These shortages, often exacerbated by system outages caused by large generating unit failures or transmission line disruptions, have significantly impacted the country's stability. This is further compounded by the ongoing political instability in Libya, which, coupled with electricity supply issues, has negatively affected oil and gas production in some of the country's largest fields. This research addresses the challenge of electricity load demand forecasting by employing Machine Learning (ML) techniques, specifically focusing on Medium Term Load Forecasting (MTLF) based artificial intelligence algorithms. The study compares the accuracy and convergence of different ML methods against actual consumption data, aiming to identify the most effective approach. Accurate load forecasting is crucial for electrical utilities like GECOL to effectively meet customer demands and optimize power generation and transmission. Focusing on Benghazi, this research pioneers the application of Machine learning techniques to predict total energy consumption and demand. The study's findings are validated against real-world data obtained from GECOL's Benghazi Regional Control Center (BRCC), demonstrating the potential of ML for improving electricity load forecasting in Libya. The study concluded with the following results: The Extra Trees Regressor algorithm produced the best results for pregnancy as a target, with an accuracy value of 85%. The Huber Regressor algorithm produced the best results for deficit quantity as a target, with an accuracy value of 77%. Keywords: GECOL, Machine Learning, Load Forecasting, Medium Term Load Forecasting, MTLF.

Synergising hydrothermal pre-treatment and biological processes for enhancing biohydrogen production from palm oil mill effluent

The high quantity of nutrient-rich palm oil mill effluent (POME) waste in the industry will have a severe negative environmental impact and a high cost of treatment. However, POME waste could be converted into bioenergy via environmentally sustainable processes. Several studies have explored using hydrothermal carbonisation for solid biomass products in biofuel production, but the potential of the liquid phase produced during these processes has received less attention. Therefore, this study aims to assess the potential of biohydrogen production from treated POME as a substrate by hydrothermal process. This study is presented in two phases: the first phase involves substrate pre-treatment using a hydrothermal process to improve biomass properties at different temperatures, and the second phase explores the potential for biohydrogen production from treated POME through dark fermentation. Substrate pre-treatment was conducted at 180, 210, and 240 °C using 100% raw POME. Next, the treated POME was incubated for biohydrogen production at 50 °C for 24hours. A microbial analysis was conducted to determine the most dominant species present in the sample. Our findings show that at 180 °C, the total chemical oxygen demand (COD) removal efficiency was 80%, and acetic acid concentration was 28%. Compared to raw POME, treated POME generated a maximum hydrogen yield and rate (HPR) of 52.19mL H2 g-1 CODrem and 0.59mL H2 mL POME-1 day-1 with a 2.32-fold and 1.59-fold increase, respectively. Meanwhile, Clostridium was a dominant bacterial species identified in the treated POME. These findings demonstrated the feasibility of implementing a hydrothermal process to treat POME and improve its biohydrogen production efficiency. The treated POME from the hydrothermal process is more homogenous and readily consumable by microorganisms used in dark fermentation. Hydrothermal pre-treatment could potentially increase the rate and efficiency of microbial digestion, leading to enhanced hydrogen production. The high COD removal efficiency during the process significantly reduces the environmental impact of POME discharge, and converting POME into a valuable resource through the hydrothermal and dark fermentation process aligns with sustainable waste management practices.