Cleaning System Research Articles

Bayesian neural networks for solar power forecasts in advanced thermoelectric systems

This study presents a Bayesian neural network for the comprehensive performance forecast of an advanced thermoelectric system featuring segmented thermocouples with variable area shape for optimal solar energy conversion. The training data is obtained from a three-dimensional numerical model developed in ANSYS software to account for the energy, exergy, and entropy flows in the thermoelectric module to maximize the power output, energy, and exergy efficiencies while minimizing the entropy generation and system irreversibilities. The thermodynamic model optimizes key parameters, including geometry (height, cross-section, and percentage skutterudite content) across realistic diverse heat fluxes and cooling coefficients. To facilitate a faster and more efficient optimization pipeline, various neural networks, leveraging algorithms like Levenberg-Marquardt, scaled conjugate gradient, and Bayesian regularization, are utilized for efficient data management and accurate performance prediction. The Bayesian neural network emerges as the most accurate and efficient model providing the lowest training and testing loss of ∼10−9. Furthermore, key findings highlight the importance of skutterudite content and solar concentration ratio. For instance, an optimal skutterudite content of 6.2 % at 25 Suns produces maximum power of 29.7 W, while 37 % skutterudite at 75 Suns minimizes exergy destruction. This study's methodology and findings contribute to designing more efficient clean energy systems and advancing sustainable energy goals.

Optimization of rural integrated energy system considering carbon emissions

Abstract The country’s objective of achieving “carbon neutrality” has necessitated transforming and upgrading rural energy systems. Establishing a comprehensive rural energy system is crucial to accelerate the development of a clean energy system in rural areas. This paper addresses the context of China’s “carbon neutrality” goal and presents a model for a rural integrated energy system that incorporates electricity, biomass energy, and PV. The model aims to optimize the capacity of energy devices while considering economic and environmental factors. Practical case studies are conducted to validate the feasibility of the proposed model.

Advanced vapour compression refrigeration system dispersed with hybrid Nanolubricants: ANN-Based approach

In this study, the R600a refrigerator’s performance evaluation has been investigated utilizing the implementation of hybrid nanolubricants (SiO2/CuO) at various quantities and mass charges. The outcomes of the experiments compared to those achieved using SiO2/CuO hybrid nanolubricants and R600a charges are examined. Hybrid nanolubricants are used as substitutes for pure lubricants, and the performance parameters are assessed against the results of the experiments. SiO2 at 0.2 g/L and CuO at 0.4 g/L hybrid nanolubricants provided an optimal cooling effect of 259 W and consumed a minimum quantity of energy of 73 W resulting in a maximum enhanced predicted COP value of 3.547 compared to experimental results. The clean lubricant system resulted in a minimum COP and refrigeration effect of 2.6 and 246 W and consumed more energy of 95 W. It has been observed that adding hybrid nanoparticles to compressor mineral oil substantially loweredcompressor energy consumption and enhancedrefrigeration effect, refrigerator performance. By using the ANN prediction approach, the use of SiO2/CuO hybrid nanolubricants led to maximum increased COP, refrigeration effect, and lower energy consumption when compared with experimental findings.

Spatial conditional convergence analysis of total factor energy efficiency based on industry transfer and technology diffusion

Measuring energy efficiency is important to achieve the goal of sustainable economic development. This paper adopts the Malmquist index to measure the trend of regional total factor energy efficiency. This paper analyses the spatial aggregation characteristics of TFEE and the impact mechanism of industrial transfer and technology diffusion on TFEE convergence from a dynamic perspective. The results of the study show that there is a large gap in TFEE between regions in China, with a spatial characteristic of “high in the south and low in the north”. In 2006–2021, the convergence rate of TFEE shows a slowing down trend. Industrial transfer has a negative effect on TFEE. The impact of technology diffusion on TFEE is different in different periods. The impact of researchers, technology market, technology introduction, and import-export on TFEE have significant differences. The convergence rate of the south is higher than that of the north. The impact of industrial transfer and technology diffusion on TFEE in the two parts is different. Policymakers should implement differentiated energy policies based on the characteristics of the industrial structure and technological development of each region. They should actively guide technological innovation, promote industrial diversification, and build a clean and efficient energy system.

Density matrix renormalization group study of the interacting Kitaev chain with quasi-periodic disorder

We document the ground state phase diagram of the one-dimensional Kitaev chain with quasi-periodic disorder in the presence of two-body interactions. Our data were obtained for systems of L=1000\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L=1000$$\\end{document} sites using large-scale density-matrix renormalization group numerics and is benchmarked against known results for the clean system. We demonstrate that moderate quasi-periodic disorder stabilizes the topological phase both for repulsive and attractive interactions. For larger disorder strengths, the system features re-entrance behavior and multiple phase transitions.Graphical abstractPhase diagram as a function of the chemical potential and the disorder strength for repulsive interactions

Optimization of wind farm energy supply system costs through machine learning

At present, the energy crisis is becoming increasingly severe, and changing the energy structure and developing clean energy have gradually become the consensus of the world. Establishing a clean energy power supply system, using clean energy for power generation, and investing in real power supply are currently unresolved challenges. Based the current high cost of new energy power supply systems, this project proposes an optimization plan based on the wind power supply system, targeting the capacity of lithium battery energy storage equipment, and constructs an optimization model for wind power supply in electric vehicle systems. In addition, in order to use lithium-ion battery devices more efficiently and ensure the evaluation of the efficiency and battery life of lithium-ion battery energy storage devices, we predicted the cyclic aging phenomenon of lithium-ion battery energy storage devices through machine learning technology. This prediction can provide suggestions and references for the use of this lithium battery.

Exploring the effects of molecular beam epitaxy growth characteristics on the temperature performance of state-of-the-art terahertz quantum cascade lasers

This study conducts a comparative analysis, using non-equilibrium Green’s functions (NEGF), of two state-of-the-art two-well (TW) Terahertz Quantum Cascade Lasers (THz QCLs) supporting clean 3-level systems. The devices have nearly identical parameters and the NEGF calculations with an abrupt-interface roughness height of 0.12 nm predict a maximum operating temperature (Tmax) of ~ 250 K for both devices. However, experimentally, one device reaches a Tmax of ~ 250 K and the other a Tmax of only ~ 134 K. Both devices were fabricated and measured under identical conditions in the same laboratory, with high quality processes as verified by reference devices. The main difference between the two devices is that they were grown in different MBE reactors. Our NEGF-based analysis considered all parameters related to MBE growth, including the maximum estimated variation in aluminum content, growth rate, doping density, background doping, and abrupt-interface roughness height. From our NEGF calculations it is evident that the sole parameter to which a drastic drop in Tmax could be attributed is the abrupt-interface roughness height. We can also learn from the simulations that both devices exhibit high-quality interfaces, with one having an abrupt-interface roughness height of approximately an atomic layer and the other approximately a monolayer. However, these small differences in interface sharpness are the cause of the large performance discrepancy. This underscores the sensitivity of device performance to interface roughness and emphasizes its strategic role in achieving higher operating temperatures for THz QCLs. We suggest Atom Probe Tomography (APT) as a path to analyze and measure the (graded)-interfaces roughness (IFR) parameters for THz QCLs, and subsequently as a design tool for higher performance THz QCLs, as was done for mid-IR QCLs. Our study not only addresses challenges faced by other groups in reproducing the record Tmax of ~ 250 K and ~ 261 K but also proposes a systematic pathway for further improving the temperature performance of THz QCLs beyond the state-of-the-art.

Design of an Arduino Uno-based Drain Cleaning System for Housing Clusters

The Design of a Drain Cleaning System in an Arduino Uno-based Housing Cluster is a research that aims to overcome the problem of obstacles in closed drainage channels in housing clusters. This research uses a design approach and implementation of an Arduino Uno microcontroller-based system integrated with ultrasonic sensors, buzzers, relays, and AC motors. The analysis method involves observation, literature study, problem analysis, solution, and conclusion. Testing is done through measuring the response of the components, the performance of the AC motor, and evaluating the entire system. The results show that the system is able to detect, measure, and overcome obstacles in drainage channels effectively. This research is expected to contribute to improving the quality of the residential environment and solving the problem of frequent flooding. By limiting the development of one unit of drainage channel cleaning tool for one house in a housing cluster, where each house will be equipped with one unit of tool installed on the water channel in front of its house. The conclusion of this research is that the system design can be an efficient and responsive solution in maintaining the smooth flow of water in the housing cluster, as well as providing convenience for residents to maintain their environment.

Sustainable Development: Driving and Inhibiting Factor Affecting the Clean Water Management System in Indonesia

This research aimed to understand the driving and inhibiting factors affecting the clean water management system in Indonesia at the ontological and sociological levels based on the public policy perspective. This research adopted a qualitative method. Data were collected through observation and documentation. Data analysis used interactive steps, which were data reduction, data display, and data verification, supported by triangulation. The results indicated that the driving and inhibiting factors affecting the clean water management system in Indonesia and its implementation provided inputs for developing better regulation and policies for state agencies as public officials and practitioners in managing the clean water management system in Indonesia.

Analytical study on improving the efficiency and environmental friendliness of solid organic fuels

The purpose of this study was to analyse methods of increasing the efficiency and environmental friendliness of the use of solid organic fuels (SOF) in electricity generation. This study employed a comprehensive approach to the analysis and optimisation of technological processes, operational systems, and environmental aspects of the use of SOF. The study found that the use of modern technologies, such as gasification and pyrolysis, considerably increases the efficiency of converting SOF into electricity. Optimisation of boiler and turbine designs and automation of fuel supply systems helps to reduce energy losses and improve overall system efficiency. It was found that the use of new materials for boilers increases their resistance to corrosion and erosion, which extends the service life of the equipment. The study also showed that the introduction of gas cleaning and secondary combustion systems significantly reduces emissions of harmful substances, which improves environmental performance. An analysis of ash utilisation opportunities showed that its use as a fertiliser or construction material is a promising area. The study proved that an integrated approach to the use of SOF can substantially increase their efficiency and environmental friendliness. The findings of the study suggest that the use of innovative methods of combustion process control allows achieving more stable and efficient power generation. It was proved that the introduction of automated monitoring and control systems reduces operating costs and increases the reliability of equipment. The study also found that the use of advanced analytical tools to predict equipment wear and tear allows for prompt preventive maintenance, which further increases the efficiency and duration of uninterrupted operation of energy systems

Solar Panel Dirt Detection and Cleaning System

The proposed solar panel dirt detection and cleaning system improves the efficiency in generating output voltage that integrates Arduino micro-controller with voltage recognition technology to enhance the efficiency of solar energy harvesting. The system utilizes voltage divider circuit to identify dirt or debris on the solar panel surface by analyzing changes in output voltage produced by the solar panel. Along with voltage divider circuit, LDR (Light Dependent Resistor) sensor is utilized to enable the cleaning process only in day time when the intensity level of the LDR is more than threshold value. Upon detection, the Arduino micro-controller triggers a cleaning mechanism to remove the identified dirt. The cleaning mechanism can be designed using actuators, brushes and other appropriate tools. The Arduino's programmability allows for customization of cleaning intervals, ensuring timely maintenance.

Clean energy technologies and energy systems for industry and power generation: Current state, recent progress and way forward

This vision article accompanies a Special Issue of Applied Thermal Engineering dedicated to the Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference series held during 2022, including the 5th SEE SDEWES Conference Vlore, 3rd LA SDEWES Conference Sao Paulo, and 17th SDEWES Conference Paphos. The article discusses a selection of papers presented at these conferences and selected for publication in Applied Thermal Engineering, all connected by a focus on clean energy technologies and systems for the advancement of sustainable thermal energy systems. This research area covers a wide range of technologies but is primarily focused on the power generation sector, energy storage and utilization, efficiency improvements, sustainable technical solutions, and the facilitation of the robust integration of renewable energy resources into wider energy systems. Additional work on previously established research topics is also present, but new directions have crystallized, especially research into thermal management for power devices and microelectronics. Based on the recent contributions to the research field presented in the included papers, this vision article discusses the development and application of cutting-edge technological solutions and identifies the challenges that require focused attention. Current shortcomings of developing and established technologies will be developed by the underlying fundamental research, reduction of prohibitively high costs, expansion of operating conditions, and the inclusion of advanced control approaches, sophisticated combinations of renewable energy sources, and modeling tools for system investigations. Scientists, researchers, and engineers can benefit from the summary of the topics at the forefront of the research field and find guidelines and ways forward for future research activities.

Development and Performance Evaluation Experiment of a Device for Simultaneous Reduction of SOx and PM

Mitigating air pollutants such as SOx and PM emitted from ships is an important task for marine environmental protection and improving air quality. To address this, exhaust gas after-treatment devices have been introduced, but treating pollutants like SOx and PM individually poses challenges due to spatial constraints on ships. Consequently, a Total Gas Cleaning System (TGCS) capable of simultaneously reducing sulfur oxides and particulate matter has been developed. The TGCS combines a cyclone dust collector and a wet scrubber system. The cyclone dust collector is designed to maintain a certain distance from the bottom of the wet scrubber, allowing exhaust gases entering from the bottom to rise as sulfur oxides are adsorbed. Additionally, the exhaust gases descending through the space between the cyclone dust collector and the wet scrubber collide with the scrubbing solution before entering the bottom of the wet scrubber, facilitating the absorption of SOx. In this study, the efficiency of the developed TGCS was evaluated, and the reduction effects based on design parameters were investigated. Furthermore, the impact of this device on ship engines was analyzed to assess its practical applicability. Experimental results showed that increasing the volume flow rate of the cleaning solution enhanced the PM reduction effect. Particularly, when the height of the Pall ring was 1000 mm and the volume flow rate was 35 L/min, the sulfur oxide reduction effect met the standards for Sulfur Emission Control Areas (SECA). Based on these findings, suggestions for effectively controlling atmospheric pollutants from ships were made, with the expectation of contributing to the development of systems combining various after-treatment devices.

Do Precipitation and Temperature Influence Perceptions of Urban Cleanliness?

Abstract This study explores the correlation between weather and the perception of urban cleanliness across the 47 largest cities in Spain. Utilizing survey data conducted by the national Consumers and Users Organization (OCU) in 2015, 2019, and 2023 to assess cleanliness perceptions, we analyze potential associations with precipitation and temperature recorded by weather stations of the Spanish Meteorological Agency. Additionally, we consider computed values of the De Martonne aridity index. The OCU data reveal regional disparities in perceived cleanliness quality. Higher cleanliness scores are obtained in cities located in the northern and north-central regions of Spain, characterized by humid and superhumid climates according to the De Martonne index. Conversely, lower cleanliness ratings are given to cities in the southern and eastern regions of Spain, where a Mediterranean climate and lower aridity index values prevail. In conducting a statistical analysis on the perception of cleanliness and variables related to precipitation and temperature, the results of the chi-square and linear correlation tests found no strong statistical correlation, although a tendency is observed. Cities with higher annual precipitation and lower values of average annual temperature tend to receive better cleanliness ratings, while drier and warmer cities exhibit the worst values of perceived urban cleanliness. Furthermore, our findings indicate that the Gompertz model effectively captures a strong statistical correlation in the relationship between cleanliness perception and the De Martonne index: As aridity increases, cleanliness perception decreases. These results are relevant for the development of future cleaning methods and systems, particularly in light of the climate change scenarios that are anticipated in the Mediterranean region due to warmer and drier conditions and, consequently, an increase in aridity.

Projected Analysis of Fulfilling Pdam Clean Water Needs in Domestic Customers of Waginopo Village, Wangi-Wangi District, Wakatobi District

Clean water is a very vital need for society, to meet the needs for drinking water, bathing, cooking, washing and including industrial needs. The existence of clean water in an area is very important considering the very dynamic activities of society. In its implementation, the drinking water supply system in Waginopo Village has not been able to run smoothly. In fact, according to a temporary survey that has been carried out, PDAM water only flows once every 2 days, once a week and the maximum flow time is only 12 hours. The aim of this research is to calculate the amount of clean water needed for the service area of Waginopo Village, Wangi-Wangi District, Wakatobi Regency until 2033. In this research, not only numerical data will be collected, but also information about what the community desires regarding the performance of the clean water distribution system, so this research study approach uses a combination of quantitative and qualitative approaches. From the results of the analysis it can be said that the total water demand of PDAM Wakatobi Regency for the Waginopo Village service area in 2033 is 867,894 m3/year. Meanwhile, based on water demand in 2024, it is 592,737 m3/year. So the calculation results from 2024 to 2033 have increased.

IoT Based Solar Panel Cleaning System

IoT Based Solar Panel Cleaning System

IoT Based Solar Tracking System with Automatic Dust Monitoring System

The most plentiful energy source available to all living things on Earth is solar energy. One way to generate power from sunshine is via the Solar Tracking System. This form of electricity generating uses renewable natural resources. It just needs the most sunshine possible to produce energy. This system tracks light levels to their maximum. By tracking the sun's movement and modifying the panel's angle and orientation accordingly, the technology maximises the effectiveness of solar panels. Using a dust sensor and a nozzle to spray water on the panel when there is a greater accumulation of dust, the cleaning system is used to remove the accumulated dust from its panel surface. Furthermore, the dust sensor continuously tracks the amount of dust accumulating on the panels and, if needed, initiates an automated cleaning procedure. Wi-Fi is used to transfer the system's data through Node MCU, allowing for real-time monitoring and analysis. The suggested system improves the efficiency of solar energy collection.

A new strategy to produce calcium carbide-acetylene from integrated multi-level low carbon construction driven by biomass

A highly efficient and clean biomass energy-based calcium carbide-acetylene production system, including low-carbon energy supply, solid waste recycling and cascade utilization of waste heat, has been developed for the conventional energy-intensive and highly polluting fossil fuel-dependent calcium carbide industry. This system supplies the carbide-acetylene production plant with energy from the gasification of biomass and converts the plant's solid waste carbide slag into the calcium feedstock required by the plant. The waste heat from the plant's high-temperature exhaust gases is recycled and used via the multi-stage heat exchange, so that the energy cascade conversion and utilization is achieved. A simulation model of the plant is created in Aspen Plus, and a mathematical model for the biomass gasification process and cycle compensation of the calcium source through the Fortran language is written and embedded. When calculating the carbon consumption and CO2e emissions of the system, it was found that the carbon consumption and CO2e emissions of the conventional process were 5.43 t Coal·t−1C2H2 and 2.25 t CO2e·t−1C2H2, respectively. However, the carbon consumption of the new process was reduced by 65.19%, and carbon emissions by 27.24% in comparison. The energy analysis shows that the energy efficiency of the system is 36.21% for the conventional process and 44.82% for the new processes. The exergy analysis of the effective energy shows that the exergy efficiency of the new process is 73.20%, which is 52.98% better than that of the conventional process. Introduction of an index, the levelized income of acetylene product (LIOA), to characterize the product income of the system. When the price of acetylene is between 2.23 $/kg and 4.19 $/kg, the LIOA for the conventional and the new processes are 1.41 $/kg to 3.38 $/kg and − 1.28 $/kg to 0.68 $/kg, respectively. It is worth noting that the critical price (PCC2H2) for products generating net revenue from the new process is 3.17 $/kg. This study is of great importance for the development of a low-carbon biomass-coupled calcium carbide-acetylene process.

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

This research paper presents the development and analysis of a multifaceted smart grid prototype. It combines various technologies for the smart grid operation. The first technology is environmental analysis of smart grid and solar panel cleaning. Secondly, radio-frequency identification (RFID)-based security and access control system has been integrated for smart grid. The third component is internet of things (IoT)-based energy monitoring and load management. For environmental analysis sensors such as temperature, humidity, light-dependent resistor, and flame sensors are connected to a NodeMCU controller for real time monitoring. Moreover, IoT based solar cleaning system is developed in the form of prototype with the help of Blynk and servo motor. The second component of prototype is smart security system which is developed with the help of Arduino and RFID module to facilitate secure access control. The third part of prototype employs voltage and current sensors with an ESP32 microcontroller and the Blynk application for real-time energy consumption analysis. This setup enables remote monitoring of voltage, power dynamics, and consumption patterns in a smart grid. It also offers an IoT based solution for load management and load shedding within the smart grid. The complete prototype overall demonstrates a comprehensive approach to 1) smart grid management, 2) environmental analysis, 3) security, and 4) energy monitoring.

Modeling, prediction and analysis of natural gas consumption in China using a novel dynamic nonlinear multivariable grey delay model

Accurately predicting natural gas consumption is essential for developing a clean, low-carbon, safe, and efficient energy system. Therefore, this paper integrates the idea of the extended logistic model into multivariable grey prediction theory. Considering the delay effect and nonlinear characteristics of the system, a novel dynamic nonlinear multivariate grey delay prediction model is constructed. Meanwhile, the algorithmic framework for solving the parameters of the new model is provided to identify and optimize the parameters of the time-delay effect of the model. The genetic algorithm was identified as the best matching optimization algorithm for the new model. Furthermore, the new model is applied to fitting and forecasting China's natural gas consumption, and the validity of the model is fully verified. Compared with other forecasting models, the new model has a lower average relative error in the fitting and forecasting stage, and, in particular, achieves a better forecasting effect with the lowest MAPEpre value (0.001 % for one-step ahead, 0.005 % for two-step ahead, and 0.746 % for three-step ahead). Moreover, the model is employed to forecast China's natural gas consumption for the next five years, providing a robust foundation for government policies related to the supply of natural gas.