Energy-efficient Technologies Research Articles

Adsorption of malachite green using waste marine cuttlefish bone powder: Experimental and theoretical investigations.

Adsorption remediation is an energy-efficient water treatment technology that utilizes the adsorption properties of a biosorbent to remove various pollutants. While many articles have explored the high surface area and adsorption capacity of activated carbon for remediating aquatic systems, few have delved into the environmental impact of its synthesis, which often involves H3PO4, a highly toxic activating agent. In this study, we present a groundbreaking alternative to activated carbon for the adsorption and remediation of aquatic waste that doesn't rely on chemical products. Our discovery that cuttlefish bone, typically considered waste, can serve as an effective adsorbent is a significant leap in eco-friendly research and a source of inspiration for future sustainable solutions. We selected Malachite Green (MG) as a cationic toxic dye to evaluate the adsorption capacity of our green adsorbent. The prepared powder underwent characterization using techniques such as FTIR, XRD, SEM, and elemental analysis. Additionally, the determination of the cuttlefish bone's zero-point charge pH revealed a value of 8.4, which influences the material's surface charge and its interaction with ions and molecules in the solution. We conducted a detailed study on the effects of solution pH, adsorbent amount, Malachite Green concentration, temperature, and contact time on the dye adsorption. The results demonstrated that the adsorption of Malachite Green by cuttlefish bone powder is an endothermic process, requiring heat input. This indicates that the adsorption efficiency increases with temperature. The significant enthalpy results obtained confirmed the endothermic nature of the process. In this study, we combined experimental and theoretical approaches to gain a better understanding of the adsorption mechanism. The results showed that Cuttlefish Bone is an environmentally friendly and highly efficient adsorbent.

Data-driven financial models for sustainable SME growth: Integrating green finance into small and medium enterprise strategies

In recent years, the increasing urgency to address climate change and environmental degradation has prompted a shift towards sustainable business practices, particularly within the realm of small and medium enterprises (SMEs). This paper explores the integration of green finance into the strategic frameworks of SMEs, examining how financial instruments and funding mechanisms can support environmentally sustainable practices while fostering economic growth. By assessing the current landscape of green finance, including green loans, bonds, and impact investing, the study identifies barriers SMEs face in accessing these resources. It highlights successful case studies where SMEs have effectively utilized green finance to implement energy-efficient technologies, sustainable supply chains, and eco-friendly product innovations. Furthermore, the paper proposes a strategic model for SMEs to incorporate green finance into their operations, emphasizing the importance of aligning with sustainability goals, enhancing stakeholder engagement, and leveraging partnerships with financial institutions. The findings underscore that integrating green finance not only contributes to environmental sustainability but also enhances the competitive advantage and resilience of SMEs in an evolving market landscape.

Clustering-based agent system (CAS) to simulate the energy-related behaviours of office occupants

Rapid urbanization and building sector growth emphasize the critical role of energy conservation in addressing global energy consumption and greenhouse emissions. Despite advancements in energy-efficient technologies, an ‘energy performance gap’ exists between predicted and actual energy use, significantly influenced by occupant behaviour. This study explores energy-related behaviour in office buildings by integrating existing behavioural theories including the Theory of Planned Behaviour and the Self-determination Theory, and construct of habit and comfort. Data from an online survey were analyzed using principal component analysis, two-step cluster analysis, and descriptive statistics, identifying three behavioral clusters: ‘Cautious Saver’, ‘Compelling Dissatisfied’, and ‘Coherent Potent’. These clusters represent distinct energy-related behaviours. A Clustering-based Agent System (CAS) was then proposed to simulate the energy-related behaviours of these clusters, offering a dynamic and adaptive modelling framework. The study advocates for a comprehensive approach, integrating behavioural theories to provide insights for developing accurate occupant behaviour models. Abbreviations: BD: Biodiesel; BR: Blending Ratio; BSFC: Brake Specific Fuel Consumption; BTE: Brake Thermal Efficiency; CI: Compression Ignition; CM: Coating Material; CO: Carbon Monoxide; CT DEE: Coating Thickness Diethyl Ether; D-Gun: Detonation Gun; DOA: Degree of Adiabacity; EGT: Exhaust Gas Temperature; HC: Hydrocarbon; HVOF: High-velocity oxy-fuel; IC: Internal Combustion; LHR: Low Heat Rejection; NOx: Oxides of Nitrogen; PVD: Physical Vapour Deposition; SEM: Scanning Electron Microscopy; SI: Spark Ignition; TBC: Thermal Barrier Coating; TMF: Thermal Mechanical Fatigue; WCO: Waste Cooking Oil; XRD: X-ray Diffraction; YSZ: Yttria-Stabilised Zirconia

Strategic Management Practices and Climate Change Mitigation of Total Energies Kenya Limited

Climate change is a major challenge to humanity’s existence as it leads to potential shift of weather patterns accelerating the vulnerability of the ecosystems. Realization of Climate change mitigation and adaptation objectives requires effective strategic management practices. Successful strategic management practices guarantee effective reduction of climate change impact and improved environmental sustainability. Failure to incorporate strategic management in the company’s short and long-term goals may result in lack of vision and failure to meet set environmental sustainability objectives within the set timelines. The aim of this study was to determine how strategic management practices has influenced the climate change mitigation within the oil sector in Kenya. The study was anchored on four theories; Triple Bottom Line Theory, Resource Based View Theory, Strategic Leadership Theory and Balanced Score Card Model. Descriptive research design was used for this study with a target populations of 360 employees of TEMK. Proportionate random sampling design was applied with 20% target sample size. Structured questionnaires with close ended question questions was employed as data collection instruments. Cronbach’s alpha measure was used to gauge the reliability of the questionnaire with result above 0.7 considered acceptable. The collected data was analyzed through coding of the questionnaire in SPSS. Multicollinearity test was done to show the occurrence of relationships among two or three independent variables. Variance Inflation (VIF) score was used for multicollinearity test and Normality test was done to ascertain the normality of error terms. A multiple regression equation was developed to show the connection between dependent and independent variables. Further analysis was done through percentages, means, standards deviations, regression and correlations. The analyzed data was presented through graphs, bar charts, histograms, pie charts and tables for easy and quick understanding. The study found out that climate financing, technological investments and climate change policies implementation have significant positive impact on the mitigation of climate change within the oil sector. The study concludes that factors such as climate financing, technological investments and climate change policies implementation have positive impact on the mitigation of climate change in the oil sector in Kenya. The study recommends that, in order to enhance climate financing, the government should establish enabling economic conditions through favorable tax regimes on projects related to climate change mitigation. Also, the study recommends that the oil sector should lobby for green financing by local banks to support investments on climate change. Further, the study recommends that in order to exploit the use of technological investments, the oil sector should enhance investment in renewable energy and energy efficient technologies.

Dynamic Window Technologies for Energy Efficiency in Condominiums in Tropical Climates

Dynamic Window Technologies for Energy Efficiency in Condominiums in Tropical Climates

Dicke superradiant heat current enhancement in circuit quantum electrodynamics

Controlling the flow of energy and heat at the microscale is crucial to achieve energy-efficient quantum technologies, for on-chip thermal management, and to realize quantum heat engines and refrigerators. Yet, the efficiency of current quantum technologies is affected by thermal noise, and efficient cooling of quantum devices remains challenging in various solid-state implementations such as superconducting circuits. Collective effects, such as the Dicke superradiant emission, have been exploited to enhance the performance of quantum devices. However, the inherently transient nature of Dicke superradiant emission raises questions about its impact on steady-state properties. Here, we study how to enhance the steady-state heat current flowing between a hot bath and a cold bath through an ensemble of N qubits, that are collectively coupled to the thermal baths. Remarkably, we find a regime where the heat current scales quadratically with N in a finite-size scenario. Conversely, when approaching the thermodynamic limit, we prove that the collective scenario exhibits a parametric enhancement over the noncollective case. We then consider the presence of a third uncontrolled bath, interacting locally with each qubit, that models unavoidable couplings to the external environment. Despite having a nonperturbative effect on the steady-state currents, we show that the collective enhancement is robust to such an addition. Finally, we discuss the feasibility of realizing such a Dicke heat valve with superconducting circuits. Our findings indicate that in a minimal realistic experimental setting with two superconducting qubits, the collective advantage offers an enhancement of approximately 10% compared to the noncollective scenario. Published by the American Physical Society 2024

A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste

The fourth industrial revolution will heavily rely on machine learning (ML). The rationale is that these strategies make various business operations in many sectors easier. ML modeling is the discovery of hidden patterns between multiple process parameters and accurately predicting the test values. ML has provided a wide range of applications in Chemical Engineering. One major application of ML can be found in the microwave-assisted pyrolysis (MAP) of lignocellulose bio-waste. MAP is an energy-efficient technology to obtain high-saturated hydrogen-rich liquid fuels. The main focus of this review study is understanding the utilization of various types of ML algorithms, including supervised and unsupervised techniques in microwave-assisted heating techniques for diverse biomass feedstocks, including waste materials like used tea powder, wood blocks, kraft lignin, and others. In addition to developing effective ML-based models, alternative traditional modeling approaches are also explored. In addition to various thermochemical conversion processes for biomass, MAP is also briefly reviewed with several case studies from the literature. The conventional modeling methodology for biomass pyrolysis with microwave heating is also discussed for comparison with ML-based modeling methodologies.

Enhanced Emission in Polyelectrolyte Assemblies for the Development of Artificial Light-Harvesting Systems and Color-Tunable LED Device.

Artificial light-harvesting systems (LHSs) are of growing interest for their potential in energy capture and conversion, but achieving efficient fluorescence in aqueous environments remains challenging. In this study, a novel tetraphenylethylene (TPE) derivative, TPEN, is synthesized and co-assembled with poly(sodium 4-styrenesulfonate) (PSS) to enhance its fluorescence via electrostatic interactions. The resulting PSS⊃TPEN network significantly increased blue emission, which is further harnessed by an energy-matched dye, 4,7-di(2-thienyl)benzo[2,1,3]thiadiazole (DBT), to produce an efficient LHS with yellow emission. Moreover, this system is successfully applied to develop color-tunable light-emitting diode (LED) devices. The findings demonstrate a cost-effective and environmentally friendly approach to designing tunable luminescent materials, with promising potential for future advancements in energy-efficient lighting technologies.

Transformation towards the future sustainable: Analysis implementation of carbon tax in ASEAN-5 countries

The emission of carbon dioxide (CO₂), which has an impact on climate change, has now developed into the global problems that impact the whole world and human life. To handle this problem, ASEAN-5, which consists of Indonesia, Malaysia, Singapore, Thailand, and the Philippines, participated in an effort to reduce donation emissions of global carbon through the initiation of tax carbon. Research Objectives This is to describe how ASEAN-5 countries respond, prepare, and make policies for implementing a carbon tax. The initiative to tax carbon in ASEAN-5 is expected to push the transformation of the economy toward sustainability and provide benefits to the economy through efficient energy and innovative technology. Using a scoping review method for various journals and articles research, the study analyzes the approach taken by each ASEAN-5 country in facing the challenge of implementing tax carbon. Study results. This concludes that the only ASEAN-5 country that has applied carbon tax is Singapore, which is still in the preparation and planning stage. Many factors have become inhibitors in implementing the carbon tax in ASEAN-5 countries. A country's dependence on material burn fossils is one of the main factors. Although so, many' green ' efforts have already been implemented by ASEAN-5 countries to help reduce global emissions. This study focuses on factors, inhibitors, and potential benefits to the economy from policy tax on ASEAN-5 carbon, mainly through improved efficiency energy and the development of energy-expected renewables​ that can push the transformation of the economy to sustainability.

Fluorinated Squareimines for Molecular Sieving of Aromatic over Aliphatic Compounds.

The development of more energy-efficient separation technologies is essential. Especially the separation of cyclic aliphatic hydrocarbons from their aromatic counterparts remains a significant challenge due to azeotrope formation and similar physical properties, often requiring energy-intensive processes. Herein, we introduce a novel class of electron-deficient macrocycles with a unique rectangular structure to optimise π···π interactions within the pore, enabling the highly selective molecular sieving of aromatic compounds from mixtures. Utilising dynamic covalent imine chemistry, the squareimine NDI2F42-based crystalline functional material is directly obtained from the reaction mixture in a single self-assembly step in high yields of 84%, alongside the larger NDI2F82 congener, which can be obtained in 69% yield. In vapour sorption and diffusion experiments, NDI2F42 demonstrates rapid adsorption kinetics with selectivities of 97:3 for benzene over cyclohexane and 93:7 for toluene over methylcyclohexane, while single-crystal and powder X-ray diffraction studies confirm that the selectivity is primarily governed by directed π···π interactions.

Review of Post-Harvest Rice Cooling for Storage in the USA

The aim of this review paper is to document the role of post-harvest rice cooling in ensuring the safe storage of rice, given the ongoing increase in rice production. The United States exports substantial quantities of rice, with milled and rough rice being the primary exports. The industry has experienced growth, propelled by rising demand, a diverse population, and international trade. Effective storage of harvested rice is essential for year-round utilization, and the USA utilizes both on-farm and off-farm in-bin storage methods equipped with features like moisture control, pest management, and temperature regulation. The adoption of rice cooling technology for storage emerges as a solution to accelerate drying, prolong shelf life, and improve overall quality. Despite challenges such as upfront costs and energy requirements, cooling technology offers benefits for both short-term and long-term storage. Integrating cooling models into mathematical simulations becomes crucial for optimizing storage conditions. Research opportunities in post-harvest rice cooling focus on energy-efficient technologies, smart sensors, modern storage structures, microbial control methods, and economic evaluations. Future trends encompass automation, intelligent monitoring, climate-controlled storage, sustainability measures, and technological integration to enhance the efficiency and security of rice storage facilities in the USA.

Guide for Nonequilibrium Molecular Dynamics Simulations of Organic Solvent Transport in Nanopores: The Case of 2D MXene Membranes.

Organic solvent nanofiltration (OSN) stands out as an energy-efficient and low-carbon footprint technology, currently reliant on polymeric membranes. With their exceptional chemical stability and tunable sieving properties, two-dimensional (2D) nanolaminate membranes present distinct advantages over conventional polymer-based membranes, attracting tremendous interest in the OSN community. Computational approaches for designing innovative 2D nanolaminates exhibit significant potential for the future of OSN technology. Imitating the pressure gradient in filtration processes by applying an external force to atoms within a predefined slab, boundary-driven nonequilibrium molecular dynamics ((BD)-NEMD) is a state-of-the-art simulation method with a proven track record in investigating the water transport in nanopores. Nevertheless, implementation of (BD)-NEMD for a broad range of solvents poses a challenge in estimating the OSN performance of theoretical membranes. In this work, we developed a (BD)-NEMD protocol that elucidates the effects of several computational details often overlooked in water simulations but are crucial for bulky solvent systems. We employed a MXene (Ti3C2O2) nanochannel as a model membrane and examined the transport of nine solvents (methanol, ethanol, acetone, n-hexane, n-heptane, toluene, ethyl acetate, dichloromethane, and water) having different properties. First, the impact of ensemble type, thermostatting, channel wall model, and restraining force constant was elaborated. After optimizing the thermostatting approach, we demonstrated that the location of the force slab particularly affects the flux of bulky solvents by changing the density distribution in the feed and permeate sides. Similarly, the uniformity of intramolecular force distribution in bulky solvents and resulting flux are shown to be prone to manipulation by slab boundaries. Next, the magnitudes of the external force generating a linear relation between the pressure gradient and solvent flux were identified for each solvent to ensure that calculated fluxes could be extrapolated to experimentally related pressures. This linear relation was also validated for a mixture system containing 50% ethanol and 50% water. We then correlated the calculated solvent permeances with various solvent properties, such as viscosity, Hansen solubility parameters, kinetic diameter, and interaction energy. Remarkably, we observed a linear correlation with an R2 value of 0.96 between permeance and the combined parameter of viscosity and interaction energy. Finally, the solvent permeances calculated with our proposed methodology closely align with the experimentally reported data. Overall, our work aims to serve as a practical guide and bridge the gap in established simulation methods that are suited for a broad range of solvents and membrane materials.

Development and validation of the motivation for electricity saving behaviour scale (MESBS) among residents living in energy-efficient buildings in Sweden

Even though energy-efficient technologies are well developed, the behaviour of building occupants plays an important role in the search for the optimum level of energy efficiency. In academic literature, however, very few studies have used psychological theories to understand individuals’ drivers of energy use behaviour when interacting with energy-efficient technologies. Specifically, there are no studies solely focused on individuals' motivations for electricity saving behaviour. This paper is based on an empirical study carried out in energy-efficient residential buildings to develop and test a new scale by applying self-determination theory (SDT). A postal survey about the Motivation for Electricity Saving Behaviour Scale (MESBS), including self-reported electricity use, was sent out to 1,084 residents living in newly built energy-efficient multifamily dwellings and eco-villages in Southern Sweden. The answers obtained from 235 participants were analysed for the reliability and validity of MESBS. The findings indicated that i) MESBS items have good internal consistency and are in line with expectations based on SDT, and ii) the autonomous motivation component of MESBS is strongly associated with electricity saving behaviour (i.e. turning off lights when not needed) in energy-efficient buildings. Our results emphasise the importance of considering individuals’ motivations for undertaking electricity saving behaviour in energy-efficient buildings. The study also provides useful information for future studies of energy-efficient buildings as well as designing new technologies and interventions to reach the optimum level of energy-efficiency.

Chinese Household Carbon Footprint: Structural Differences, Influencing Factors, and Emission Reduction Strategies Analysis

The wide variation in household characteristics, such as household size, income, and age, can lead to significant differences in carbon footprints. Based on data from 1132 Chinese households in 2021, this study examines the structural differences, multiple influencing factors, and mitigation strategies of household carbon footprints (HCFs) in China. The results indicate that indirect emissions, primarily from energy and food consumption, account for the largest share of household carbon footprints, making up over 65% of total emissions. Households with lower carbon footprints are characterized by a per capita living area of less than 20 square meters, rural residences, and shared living arrangements. Carbon footprints for the elderly and minors are lower than adults, while households with higher monthly incomes have the highest carbon footprints. The Multivariate Analysis of Variance (MANOVA) reveals that the main factors influencing HCF include household size, income, and single status, with a more pronounced impact on affluent households than on average households. High-income households have the potential to reduce their carbon footprints through investments in energy-efficient technologies, whereas low-income households are more susceptible to the effects of household size and geographic location. It is recommended that policymakers adopt differentiated measures, such as setting higher reduction targets for larger and wealthier households while providing incentives and technical support to low-income households to achieve meaningful carbon reductions. More effective and equitable low-carbon policies can be formulated by addressing these structural disparities and leveraging the unique characteristics of different household types.

Energy Consumption and Energy Costs Based on Time-of- Day Tariffs in Seafood Processing Units in Kerala, India

Energy conservation in seafood processing is crucial due to the highly energy-intensive nature of its main stages such as pre-processing, processing, and storage, which must operate under cold chain conditions. The variability of energy tariffs based on Time of Day (TOD) significantly impacts the economic sustainability of these operations. This study evaluates the electrical energy consumption patterns of seafood processing units in South India, categorized by production capacity. Primary data were collected from 10 seafood processing units, assessing energy consumption during Normal Load Time (NLT), Peak Load Time (PLT), and Off-Peak Load Time (OLT) over a one-year period. Resultsrevealed that smaller capacity units (<30 T/D) accounted for 56% of total production during PLT, despite energy tariffs being highest during this time.These low-capacity units also exhibited higher mean energy consumption and costs compared to larger units, suggesting inefficiencies. The findings highlight the need for optimized energy management strategies, such as rescheduling production to lower-tariff periods and adopting more energy-efficient technologies. Implementing such measures could reduce energy costs and improve the overall profitability of seafood processing operations, particularly for smaller units.

Computational Exploration of Adsorption-Based Hydrogen Storage in Mg-Alkoxide Functionalized Covalent-Organic Frameworks (COFs): Force-Field and Machine Learning Models.

Hydrogen is a clean-burning fuel that can be converted to other forms. of energy without generating any greenhouse gases. Currently, hydrogen is stored either by compression to high pressure (>700 bar) or cryogenic cooling to liquid form (<23 K). Therefore, it is essential to develop safe, reliable, and energy-efficient storage technology that can store hydrogen at lower pressures and temperatures. In this work, we systematically designed 2902 Mg-alkoxide-functionalized covalent-organic frameworks (COFs) and performed high-throughput (HT) computational screening for hydrogen storage applications at 111, 231, and 296 K. To accurately model the interaction between Mg-alkoxide sites and molecular hydrogen, we performed MP2 calculations to compute the hydrogen binding energy for different types of functionalized models, and the data were subsequently used to fit modified-Morse force field (FF) parameters. Using the developed FF models, we conducted HT grand canonical Monte Carlo (GCMC) simulations to compute hydrogen uptakes for both original and functionalized COFs. The generated data were subsequently used to evaluate the materials' gravimetric and volumetric storage performance at various temperatures (111, 231, and 296 K). Finally, we developed machine learning (ML) models to predict the hydrogen storage performance of functionalized structures based on the features of the original structures. The developed model showed excellent performance with a mean absolute error (MAE) of 0.061 wt % and 0.456 g/L for predicting the gravimetric and volumetric deliverable capacities, enabling a quick evaluation of structures in a hypothetical COF database. The screening results demonstrated that the Mg-alkoxide functionalization yields greater improvements in volumetric H2 storage capacities for COFs with smaller pores compared to those with larger (mesoporous) pores.

Effect of Architecture and Inference Parameters of Artificial Neural Network Models in the Detection Task on Energy Demand

Artificial neural network models for the task of detection are used in many fields and find various applications. Models of this kind require adequate computational resources and thus require adequate energy expenditure. The increase in the number of parameters, the complexity of architectures, and the need to process large data sets significantly increase energy consumption, which is becoming a key sustainability challenge. Optimization of computing and the development of energy-efficient hardware technologies are essential to reduce the energy footprint of these models. This article examines the effect of the type of model, as well as its parameters, on energy consumption during inference. For this purpose, sensors built into the graphics card were used, and software was developed to measure the energy demand of the graphics card for different architectures of YOLO models (v8, v9, v10), as well as for different batch and model sizes. This study showed that the increase in energy demand is not linearly dependent on batch size. After a certain level of batch size, the energy demand begins to decrease. This dependence does not occur only for n/t size models. Optimum utilization of computing power due to the number of processed images for the studied models occurs at the maximum studied batch size. In addition, tests were conducted on an embedded device.

Combining energy generation and radiant systems: Challenges and possibilities for plus energy buildings

Radiant heating and cooling (RHC) systems are being more widely adopted considering the well-known technical advantages: increased thermal comfort, space saving, and reduced energy use. Since the building sector is currently one of the largest consumers of fossil fuels, many directives and regulations have been enacted to address the intense concern about energy use for space conditioning.Even though radiant systems are considered as an energy efficient technology for building heating and cooling, more effort is needed to fulfil the zero energy requirements outlined by recent standards and directives. Renewable Energy Sources (RES) are an effective solution to avoid using finite fossil fuels and related geopolitical issues enhanced by the recent world conflicts. Despite being primarily intermittent and subject to economic and regional constraints, RES offer suitable temperature levels to supply low temperature heating and high temperature cooling operation, a major advantage of RHC system.Although a limited number of studies directly report energy savings or CO2 emission reduction as the main outcomes of the research related to this combination, valuable insights have been obtained for the present review. Primary energy can decrease between 40% and 80% with different integration of RHC, photovoltaic, heat pumps and district heating. TABS can lead to load shifting up to 100%, allowing an increased self −consumption of renewable energy.This paper provides evidence on whether coupling radiant systems with renewables is a promising strategy for achieving nearly-zero annual energy balances in building stocks. It investigates recent trends, limitations and potential to support decarbonization goals.

A deep treatment process for chemical polishing wastewater towards resource recovery: Optimization and performance

The chemical polishing industry generates abundant wastewater that contains high concentrations of phosphoric acid and metal ions. In light of the rapid growth of the economy and the stringent demands for sustainability, developing cost-effective and energy-efficient technologies for wastewater treatment is crucial. Herein, an EPRC-ICT-ANT process integrating electrodialysis, iron contact, and alkali neutralization, was proposed to deeply treat chemical polishing wastewater and achieve optimal recovery of acid, phosphorus, and aluminum. The results indicated that with the electrochemical phosphoric acid recovery cell (EPRC), the maximum recovery efficiency and final phosphoric acid concentration were 34.6 % and 25.2 g/L. Following the iron contact tank and alkali neutralization tank (ICT-ANT) treatment, a remarkable 99.17 % removal of Al3+ and 49.5 kg/t wastewater yield of AlPO4 were achieved. Simultaneously, the released Fe2+ and remaining PO43- triggered the rapid formation of vivianite with a yield of 120.7 kg/t wastewater. This comprehensive treatment strategy led to the near-complete removal of Al3+ and PO43- (∼100 %) with an impressive recovery of Al3+ (∼100 %) and PO43- (75.7 %), emphasizing its immense potential for chemical polishing wastewater treatment.

RESEARCH OF THE ACTIVITIES OF PEASANT FARMS IN KAZAKHSTAN IN THE CONTEXT OF EFFECTIVE ENERGY CONSUMPTION

Some of the concepts that can be identified with agriculture in Kazakhstan include farmers and livestock breeders. Nowadays, the concept of agriculture is inextricably linked with the concepts of efficient energy consumption and "green" farming, thus there is a need to improve the use of renewable energy sources. Ensuring the efficient use of energy has advantages in the form of lower production costs and social benefits in the form of reducing carbon dioxide emissions to a level that does not harm the environment, in support of sustainable development of agricultural regions. The purpose of the article is to analyze the impact of the use of renewable energy sources on the development of peasant farms. In particular, it is planned to identify factors related to renewable energy sources in terms of agricultural development, profitability, and their impact on the environmental component. In this context, an analytical comparison of installed renewable energy capacities and indicators of reducing the cost of CO2 emissions due to energy-efficient technologies of the new generation was carried out.