Reduce Energy Consumption Research Articles

Data-driven system identification and model predictive control of pneumatic conveying using nonlinear dynamics analysis for optimised energy consumption

Pneumatic conveying systems provide secure transportation of particulate material and a dust-free environment. These systems face high energy consumption, material degradation, and pipeline blockages. This research presents an innovative solution by integrating nonlinear dynamics analysis of electrostatic sensor data, including chaos and recurrence quantification analysis, sparse identification of nonlinear dynamics with control (SINDYc) and model predictive control (MPC). The Lyapunov exponent, approximate entropy and recurrence rate of electrostatic sensor data reveal the chaotic nature of gas-solid flows. MPC framework was tailored for real-time optimisation of a pneumatic conveying system. SINDYc system models were developed using data collected from an open-loop control pneumatic conveying process to conduct MPC simulations and select an appropriate model for real-time control. This research illustrates the potential of integrating nonlinear dynamics analysis, SINDYc integrated and MPC for enhanced system performance, showcased a significant reduction in energy consumption without compromising the system's efficiency or reliability.

Building shape optimization based on interconnected embodied and operational energy and carbon impacts

Strategic building design decisions informed by a comprehensive life cycle impact assessment hold substantial potential in addressing global energy consumption and carbon emissions, with buildings accounting for 40% of energy consumption and 37% of emissions. While building operational energy (OE) has traditionally received greater attention, the dynamic trade-offs between OE and embodied energy (EE) in building design are often overlooked. Design decisions, such material selection and building shape, can present conflicting considerations between OE and EE. Addressing these impacts in isolation may fail to achieve comprehensive reductions in energy consumption and carbon emissions. This study highlights the necessity of a holistic approach to design decisions such as building shape and material selection, emphasizing the importance of considering both OE and EE simultaneously. Our literature review confirms a gap in studies that consider these factors together in the context of building shape optimization. To analyze the interconnectivity of building shape design, material choice, and the trade-offs between OE and EE, we devised two parametric test cases with identical features but using two different insulation materials: hemp-based and glass wool. The analysis allows us to (1) evaluate the impact of integrating total lifecycle impact on building shape design optimization, in comparison to designs that focus solely on OE; (2) assess how the selection of materials, specifically hemp-based versus glass wool insulation, influences optimized building shape and carbon emissions reduction; and explore the benefits of bio-based materials by analyzing performance variations in the two insulations case studies. Our findings reveal that although OE typically has a larger impact over a building’s lifetime, the optimal building shape can still vary significantly based on the EE contribution. The benefit of bio-based material is context-dependent, varying with factors such as the building shape discussed in this paper, as well as other factors such as climate. In our test cases, we observed that shape variations resulted in up to a 17.9% change in OE due to different building shapes, and a 60.7% variation in the envelope’s surface area, while the building area remained unchanged. This expanded understanding will facilitate more informed and sustainable decision-making in the construction and design industries, addressing the often-overlooked interconnectivity of OE and EE.

ANN-CFD research on key parameters of stirred tank based on multi-objective optimization and decision makers’ preferences

Utilizing the Multi-Objective Optimization (MOO) model and the Multi-Criteria Decision-Making (MCDM) method provides solutions for the rarely studied issue of trade-offs between energy consumption and performance in stirred tanks for decision makers across various industrial sectors. MOO optimizes predictions from a data prediction model to generate a Pareto solution set. By examining the influence of subjective weights on dependent variables, target weight proportions for different preferences are established. The MCDM method is then applied to select optimal solutions from the Pareto set for distinct industrial applications. Notably, the optimal solution for preference Np results in an 86.5% reduction in energy consumption, while the solution for preference Nq achieves an ideal state within the Pareto set. Additionally, the optimal solution for preference σ decreases the standard deviation of solids to 9.93% of the baseline value, demonstrating effective energy consumption optimization.

Research on the prediction and realization path of urban carbon peak along the Yellow River Basin

China and the international community attach great importance to sustainable development goals such as peaking carbon emissions. As an important energy base in China, the Yellow River Basin has significant capacity for reducing carbon emissions and increasing carbon sinks. To accelerate the achievement of carbon peak targets, social and natural development data as well as carbon emission data of 56 cities along the Yellow River Basin from 2000 to 2021 were analyzed. Based on the evaluation indicators of the model, the PSO-XGBoost-RF model was selected from the comparative models to predict the carbon peak of cities in the Yellow River Basin under different paths. Under the current economic and social development, cities along the Yellow River Basin in China are facing huge pressure to reduce emissions. It is expected to achieve a carbon peak in 2033, with a peak of 2,051,320,000 tons, so the target of reaching the peak of carbon emissions before 2030 will not be accomplished. But through comprehensive optimization of the industrial structure and reduction of energy consumption, cities along the Yellow River Basin as a whole may achieve carbon peak by 2026, with a peak of 1,917,132,000 tons.

Scalable membrane-assisted ion exchange (MEM-IE) strategy for organic acid purification in biorefinery process

The transition towards a carbon-neutral society necessitates radical approaches in the bio-refinery pipeline, particularly in the production of organic acids. The current downstream process from a dilute fermentation broth is limited by the extensive use of acids and bases, along with heavy reliance on energy-intensive distillation. In this work, we propose an entirely membrane-based process to purify organic acids (e.g., gluconic acid) from a crude solution of catalytic dehydrogenation of glucose. To facilitate downstream purification, we introduce an innovative membrane-assisted ion exchange (MEM-IE) strategy, which is a scalable process that can protonate ionic compounds entirely in the solution phase. Instead of a solid ion exchange resin, a bulky yet soluble acidification agent is used to protonate the target compound, which can be easily separated via a size exclusion membrane. We selected non-toxic poly (4-styrene sulfonic acid) (H-PSS, 75 kDa) as the acidification agent to selectively protonate gluconate ions and to enable facile fractionation. The proposed MEM-IE strategy can overcome the scale-up limitation of traditional solid ion exchange resins and can be applied to many types of ionic compounds. The versatility of the proposed process was also demonstrated on formate and lactate compounds. A techno-economic evaluation using the Verberne cost model showed that the proposed process achieves an 80 % reduction in energy consumption compared to the fermentation-based process, and the return on investment (ROI) of a 330 ton-per-day plant was less than a year. The proposed membrane-based process for the purification of organic acids, particularly the MEM-IE strategy, offers a sustainable and energy-efficient downstream separation platform.

Impact of employing phase change material in building wall on energy consumption and thermal comfort level

Energy crises are becoming a problem for many, most of the energy being produced is utilized in buildings. Building design and materials that are used in buildings play a vital role in saving electricity. Sustainable cities are one of the eleventh goal of Sustainable Development Goals to be achieved and this study is in context of SDGs for developing tools to create this globe sustainable and efficient society for humans. Phase change material is a type of material that can absorb and release heat energy as it changes between solid and liquid states. This ability to store and release thermal energy makes PCM a promising material for use in building insulation, Heating Ventilation Air Conditioning Systems, and other energy efficient applications. The use of PCM in buildings has the potential to significantly reduce energy consumption and improve thermal comfort levels. By incorporating PCM into building envelopes, such as walls, roofs, and floors, the material can absorb excess heat during the day and release it at night, helping to regulate indoor temperatures and reduce the need for heating and cooling systems. This study investigates the impact of using PCM in building envelopes. In this regard office building rooms are modelled in CAD software and are simulated in energy plus software. The simulation results are validated by considering the experiment and numerical study as a reference from paper. Model room is simulated for Hyderabad, Pakistan. Building envelopes without PCM have conduction rate of around 106.6 W/m2, whereas building envelopes with PCM have conduction rate around 42 W/m2. This shows that utilization of PCM reduced heat conduction up to around 40%. PCM reduced energy consumption of around 33.25 kWh/year/m2. Results show that without PCM air temperature was above the thermal comfort value. it reached 300C in hot months, it reduced up to 270C with the incorporation of PCM.

An Energy Efficient Prediction Based Protocol for Target Tracking in Wireless Sensor Networks

Target tracking is one of the most attractive applications of wireless sensor networks, used for estimating the moving target's position accurately. A primary challenge in this domain is achieving precise target path estimation while conserving energy resources. This paper introduces an energy-efficient target tracking protocol in wireless sensor networks, considering both accuracy and reduced energy consumption. The protocol uses the Kalman filter to estimate the target's position and predict the subsequent step of its path. In each step of target tracking, a selected sensor, named the ‘leader’ performs computations for position estimation and path prediction, while two other sensors, known as ‘assistants’ help the leader in the tracking process. Leader selection within the protocol is performed in two phases: an initial phase occurring upon the target's entry to the network and a subsequent phase named the forced handoff phase. The forced handoff phase performs the selection of a new leader when either the target exits the sensing range of the current leader or the leader's energy decreases significantly. Although the proposed protocol is a new work, it can be considered as an improvement of the PPCP protocol by adding several changes and also replacing the binary variational filter with the Kalman filter. The efficiency of the proposed protocol is evaluated through simulations in Matlab. Results demonstrate the protocol's ability to achieve high-precision target tracking while maintaining low energy consumption. Comparative analysis shows its energy efficiency, which significantly increases the network lifetime.

Study on a novel thermal management system and heat recovery strategy of range extend electric vehicle

The integrated thermal management system (ITMS) can effectively improve the pure electric range of the vehicle. This work proposes a ITMS based on a super bottle. In this work, the power battery temperature is subdivided into five stages and a control strategy for heating the battery and cabin with dual heat sources in a low-temperature environment is developed. Then a numerical integrated simulation model of the ITMS is developed and experimentally verified in an environmental chamber. The experimental and simulation conditions used throughout the study are set to an ambient temperature of 0°C. At the same time, due to the better sampling of data under 40 km/h, a constant speed of 40 km/h is used. At 0 °C, through 660 s of 40 km/h conditions, the results show that the ITMS can heat the battery to 15 °C in about 20 minutes at a low temperature of 0°C, and heat the cabin to 22 °C in 10 min. The ITMS also improves battery warm-up efficiency by 16.7% and reduces energy consumption by 5.5% compared to the original system. The ITMS is 17% more expensive than the original system. In addition, the low-temperature electric range has been increased by 3.3 km, and the cost of increasing the range by integrating the thermal management components is approximately 28 ¥/km.

Improving thermal environment and ventilation efficiency in high-temperature excavation tunnels via an innovative heat insulation and cooling baffle

An increasing number of tunnels inevitably encounter high-temperature environments induced by elevated geotemperatures. The energy demand for ventilation and cooling in excavation tunnels increases significantly as the temperature of the surrounding rock increases, thereby hindering the sustainable exploitation of deep resources. This paper presents an optimization method for airflow organization in excavation tunnels that integrates thermal insulation and cooling (TIC) baffles. The proposed approach significantly improves the cooling effect of the auxiliary ventilation system while reducing energy consumption. This method offers the advantages of simplicity, convenience, and cost-effectiveness. A multifield coupling model using COMSOL software was developed and validated to analyze the system, and the application scenario was explored. Several ventilation scenarios were examined, highlighting that TIC baffles effectively reduce the heat release from the surrounding rock and lower the airflow temperature in personnel areas in the excavation tunnel. By implementing TIC baffles, the average airflow temperature in the excavation tunnel decreases by 1.5 °C to 1.8 °C, resulting in a saving of approximately 5.11 kW in cooling energy. Increasing the circulating water flow in the heat-exchange pipe of the TIC baffles or reducing the initial circulating water temperature can enhance the cooling capacity of the TIC baffles and lower the tunnel airflow temperature to a certain extent. The distance between the TIC baffles and surrounding rock significantly affects the airflow temperature between the left and right TIC baffles. An excessive length of TIC baffles (L > 7 m) leads to heat accumulation and high-temperature airflow in localized areas. In This study, a method for optimizing the thermal environment and saving energy in high-temperature excavation tunnels is proposed.

Mapping Age Differences in Brain Energy Metabolites and Metabolic Markers of Cellular Membrane Production and Degradation With 31P Magnetic Resonance Spectroscopy.

Using Phosphorus Magnetic Resonance Spectroscopy (31P MRS), we examined five metabolites associated with brain energy cycle, and cellular membrane production and degradation in 11 brain regions of 48 children (age 6-15), and 80 middle-aged and older adults (age 52-87). Levels of phosphomonoesters (PMEs) and phosphodiesters (PDEs), gamma plus alpha adenosine triphosphate (γαATP), phosphocreatine (PCr) and inorganic phosphate (Pi), were residualized on the total amplitude value. PMEs were greater in children compared to adults, whereas PDEs showed the opposite age difference. Higher γαATP and lower Pi were found in children compared to adults. The age group differences were particularly salient in the association cortices and anterior white matter. Among children, age correlated negatively with PMEs and positively with PDEs in association cortices. Compared to children, adults had lower intracellular pH. The results suggest reduction in membrane synthesis and increase in membrane degradation in adolescents and to a greater degree in adults compared to younger children. Concomitant reduction in γαATP and increase in Pi are consistent with reduced energy consumption in adolescents and further drop in middle-aged and older adults, although it is impossible to distinguish declines in energy supply from reduced demand due to shrinking neuropil, without longitudinal studies.

Exploration and multi performance evaluation of process intensification for separation of ethyl tert-butyl ether/ethanol from wastewater

Ethyl tert-butyl ether is a green gasoline additive that enhances fuel efficiency and can play an essential role in improving fuel performance. This study explored the separation of wastewater containing ethyl tert-butyl ether and ethanol by changing the pressure and adding reactions based on extractive distillation, using the synergistic effect of the entrainer, pressure, and chemical reaction. By utilizing coupling technology and process intensification concepts, developed a comprehensive series of intensified processes that have clarified the interaction mechanisms between the extractive distillation process (EDP), pressure swing extractive distillation process (PSEDP), and reactive extractive distillation process (REDP). Studying the separation and purification mechanisms of products and intermediates during the reaction process provides theoretical and practical support for optimizing chemical processes. Comparative analysis showed that the reactive extraction pressure swing distillation process (REPSDP) had preeminent economic and environmental benefits. The total annual cost of PSEDP, REDP, and REPSDP was reduced by 5.88 %, 1.68 %, and 10.08 %, respectively, compared with EDP. The gas emissions of PSEDP, REDP, REPSDP, and REPSDP with heat integration process (HI-REPSDP) are all lower than those of EDP. HI-REPSDP saves 47.59 % of gas emissions compared to EDP, highlighting its environmental benefits. HI-REPSDP has the best energy efficiency, reducing energy consumption by 47.58 % compared to EDP, significantly improving the energy utilization rate. It was found that the introduction of heat integration technology had a significant improvement effect. This research has crucial theoretical and practical value and provides guidance and reference for achieving a clean and efficient production process.

Application of low energy building thermal energy optimization based on image recognition and light sensors in urban green environment planning

The paper analyzes the application of thermal energy optimization scheme based on image recognition and light sensor technology in low-energy buildings, in order to improve building energy efficiency, reduce energy consumption and provide scientific basis for urban green environment planning. In this paper, image recognition technology is used to monitor the light change in the external environment of the building, and the indoor light intensity data is obtained by the light sensor in real time. Combined with big data analysis, building heat optimization model is established, intelligent control system is designed, and the heating and cooling system of the building is dynamically adjusted, so as to achieve efficient use of heat energy. The experimental results show that the thermal energy optimization method based on image recognition and light sensor has a good application prospect in low-energy buildings, which not only effectively reduces energy consumption, but also contributes to the sustainable development of urban green environment.

Fabrication of cobalt oxide nanosheets using Withania somnifera root extract for degradation of organic pollutants

In today’s scenario, material science emerges as pivotal players, as it is an emerging and fast-growing interdisciplinary field. Nanomaterials are increasingly popular among researchers due to their unique physicochemical properties and multifaceted applications. Recently, researchers are exploring more sustainable approaches for the synthesis of nanomaterials due to its numerous advantages. In this context, present investigation reports an approach for the synthesis of cobalt oxide nanosheets (Co3O4 NSs). This approach leverages environmentally friendly and sustainable methods, minimizing the use of hazardous chemicals and reducing energy consumption. Hence it involved Withania somnifera (ashwagandha) root extract as a greener reductant as well as stabilizing agent. The synthesized Co3O4 NSs were thoroughly analyzed using various techniques, including ultraviolet-visible (UV–vis) spectroscopy, fourier-transform infrared spectroscopy (FTIR), powder x-ray diffraction (PXRD), and field emission scanning electron microscopy (FE-SEM). A sharp absorption peak at 252 nm with a tail towards higher wavelength reveal the formation of Co3O4 NSs. The diffraction pattern reveals a face centered cubic structure of Co3O4 NSs. Morphological studies confirmed the substantial surface area of Co3O4 NSs which enable us to perform the catalytic degradation of azo dye, i.e., methyl orange. It provides that 10 mg of Co3O4 NSs is sufficient to degrade a 10 ppm aqueous methyl orange solution by 75.82 % in the dark and by 96.12 % under sunlight exposure. Thus, this study offers an excellent pathway for the synthesis of Co3O4 NSs and demonstrates their potential as a promising material for future catalytic applications.

Adaptive control of nonlinear time-varying systems with unknown parameters and model uncertainties

This paper investigates the adaptive control problem for nonlinear time-varying systems with unknown parameters and model uncertainties. A novel class of switching functions is designed, and its construction method is detailed, along with a proof of the continuity of its n−1 order derivatives. Two simple examples are provided to illustrate how the proposed congelation of variables method handles unknown high-frequency time-varying parameters in both the feedback and input paths. A new neural network control scheme is then developed, integrating an adaptive neural network controller with a robust controller. The smooth transition between these two controllers is ensured by the novel switching function, which guarantees global system stability. Furthermore, by combining the congelation of variables method with adaptive backstepping, a new adaptive tracking control scheme is proposed. This scheme effectively handles unknown high-frequency time-varying parameters while achieving asymptotic tracking of arbitrary reference signals. Simulation results show that the proposed novel adaptive control method delivers superior control accuracy while reducing energy consumption: it achieves an order of magnitude improvement over the traditional adaptive robust control method and two orders of magnitude improvement over the conventional sliding mode control method.

A flat sealing test device using water as the medium

Abstract The planar sealing test device uses water as the working medium to determine the sealing of the test plane, Water has a sealing effect and an intuitive visual effect. Fix the test piece on the testing device platform, and the working medium water moves upward under the thrust of the piston rod to fully cover the test piece. Increase a certain amount of air pressure inside the test piece, check for bubbles around the test piece, and determine the sealing of the plane. This design compensates for the shortcomings of flat sealing inspection equipment, and more conveniently and efficiently solves the sealing inspection problem before the assembly of flat sealing components, reducing energy consumption, saving resources, and improving production efficiency.

A Multi Moving Target Localization in Agricultural Farmlands by Employing Optimized Cooperative Unmanned Aerial Vehicle Swarm

The paper proposes an original method for employing optimised cooperative swarms of Unmanned Aerial Vehicles (UAVs) to localise multiple moving objects in agricultural farmlands. Crop Monitoring (CM), targeted fertilizer distribution, and Livestock Management (LM) are some of the Smart Farming (SF) applications of UAVs. However, the ever-changing nature of agricultural settings makes it challenging to set up UAV swarms. Detecting multiple evolving objectives in dynamic environments is complicated, and conventional methods are regularly optimized for single objectives, such as area or reduced Energy Consumption (EC), which is unsuitable. This research recommends a Multi-Objective Evolutionary Algorithm (MOEA) as a model for UAV swarms to balance task service, communication, and EC during the investigation. The approach paves the method for innovation in the agricultural sector by optimizing tasks in real-time, addressing unpredictable targets, boosting productivity, and reducing costs. The study’s findings present optimism for smart farm management and accurate SF by improving UAV systems’ response time and scalability.

Two Energy-Efficient Backhauling Solutions for Small Cell Networks of 5G Using Green Communications

To meet the problem of ever-increasing wireless data congestion, the fifth-generation (5G) wireless communication system is projected to employ tiny cellphone networks for the needs of consumers heavily. One of the most significant elements of 5G networks will be sustainable interactions, as the quantity of power utilized by the information and communication technology (ICT) sector is expected to rise significantly by the end of the century. Therefore, scientists have focused much attention in recent years on developing strategies for designing small cell networks that use electricity optimistically. In addition, service providers need energy-efficient backing-up technologies to aid in using packed tiny cells. This research presents an interaction model that is well-suited to 5G HetNets and has a low power footprint. The model here accounts for an internet connection’s contact and backup portions. We create and present a mathematical framework to calculate the optimum number of tiny mitochondria that need to be maintained active at numerous hours of the day to minimize power consumption while still satisfying quality of service requirements set by customers. Our investigation into the backhaul’s electrical consumption led us to discover and put forward two energy-saving backing-up methods for 5G HetNets. Computer simulations show that the provided sustainable communication framework can reduce energy consumption by as much as 49% compared to the status quo.

Multilayer decoupling control of photovoltaic integrated heat pump based on expected value

Abstract In this paper, a multilayer decoupling control method for photovoltaic heat pumps based on expected values is proposed, which aims to achieve the optimal operation of the photovoltaic heat pump system and photovoltaic heat pump system. This method predicts the output energy of photovoltaic photovoltaic system and realizes multilayer decoupling control based on the load demand of the heat pump system. In order to adapt to the change in environment, the adaptive fuzzy PID compound control method is adopted to optimize the control strategy. Experimental results show that this method can effectively improve the system efficiency, reduce energy consumption, and demonstrate good adaptability and stability.

Sequential extraction of hawthorn pectin: An attempt to reveal their original mode of being in plants and functional properties

Hawthorn is rich in pectin, which is much higher than most cultivated fruits, but conventional extraction methods do not meet the requirements of low energy consumption and green production. Pectin in hawthorn is divided into soluble and insoluble parts, and with the ripening of hawthorn, the original pectin is converted into soluble pectin and pectic acid under the action of enzymes. Therefore, based on the characteristics of hawthorn pectin, this study sequentially extracted hawthorn pectin using water-soluble pectin (WSP) and hot acid-soluble pectin (HAP) method, verifying the feasibility of extracting hawthorn pectin with pure water at room temperature, and systematically analyzing and comparing the physicochemical properties and functional characteristics of the two methods. The combination of texture analysis and gel rheology revealed that WSP formed a more uniform and dense network structure during the gelation process. Additionally, microscopic observations and emulsification index results indicated that the emulsion prepared with WSP (WSE) had a smaller particle size and better stability. This indicates that hawthorn pectin is suitable for extraction with pure water at room temperature, which can maintain its good physical properties while reducing energy consumption, providing a new approach for the large-scale extraction of pectin in the food industry.

Soft collision avoidance based car following algorithm for autonomous driving with reinforcement learning

By safety supervision on dangerous driving behaviors, emergent braking in autonomous vehicles can effectively prevent collisions when using the car following algorithm based on deep reinforcement learning. However, the significant deceleration associated with emergent braking often results in an uncomfortable driving experience and high energy consumption. To address this issue, a soft collision avoidance based car following algorithm is proposed. Different from emergent braking, our approach introduces a deceleration adjustment value to the current acceleration output. This adjustment value is calculated by considering safe distance with attenuation coefficient in terms of multi-step prediction, while the attenuation coefficient and the predicted time step are discussed in detail. Comparative analysis, including statistical results and representative cases, demonstrates that the proposed algorithm significantly enhances driving comfort (improve 37.341 %) and reduces energy consumption (improve 11.244 %) without increasing collision risks.