International Journal of Energy and Environmental Engineering
The International Journal of Energy and Environmental Engineering (IJEEE) is a multi-disciplinary, single-blind peer-reviewed open access journal, covering all areas of energy and environment related fields that apply to the science and engineering communities. IJEEE aims to promote rapid communication and dialogue among researchers, scientists, and engineers working in the areas of energy and environmental engineering. The journal provides a focus for activities concerning the development, assessment and management of energy and environmental engineering related programs. The journal aims at becoming an important factor in raising the standards of discussion, analyses, and evaluations relating to energy and environment programs. This journal operates on an open access model and charges an Article Processing Charge (APC) to cover the costs of publication and ensure free and permanent access to published articles. We are committed to supporting researchers worldwide and offer APC waivers for authors from low- and middle-income countries, as well as in cases of financial hardship. Waiver requests are considered on a case-by-case basis and do not influence editorial decisions. For more information on APCs and waiver eligibility, please visit our APC & Waivers Policy page. Journal DOI: 10.57647/ijeee Journal Abbreviation: Int. J. Energy Environ. Eng. Journal Title Acronym: IJEEE Journal Owner: Islamic Azad University Publisher: Islamic Azad University, Co-publishing, OICC Press, UK Director-in-Charge: Farivar Fazelpour, PhD
The STMO Model for Balancing Growth and Conservation in Juneau, Alaska
Juneau, Alaska, faces rapidly increasing visitor numbers, generating substantial economic gains while intensifying environmental pressures and social tensions. To address these challenges, this study develops a dynamic multi-objective optimization model that jointly considers economic revenue, ecological degradation, and social carrying capacity. The model integrates feedback loops and reinvestment strategies, demonstrating how visitor caps, tax policies, and targeted expenditures influence long-term system outcomes. Sensitivity analysis and simulation results identify the parameters most critical to sustainable tourism management, including infrastructure efficiency, environmental mitigation rates, and community development investment. Although Juneau serves as the case study, the framework is generalizable and can be adapted to other overtourism destinations by calibrating site-specific constraints and priorities. The findings highlight actionable pathways for balancing growth and conservation, while offering methodological and policy insights that extend to the field of overtourism research globally. Although the literature on overtourism has examined economic, social, and environmental impacts, it lacks a dynamic, multi-objective framework capable of capturing feedback loops and long-term trade-offs. Existing models are largely static or single-dimensional, leaving destinations without tools to understand how economic, social, and environmental outcomes interact over time. This study fills that gap by introducing a dynamic optimization model integrating revenue generation, ecological degradation, and social carrying capacity, offering a comprehensive structure for long-term sustainable tourism planning.
Copyright (c) 2024 Jinyu Wang, Zeren Yu, Jing Li, Yunpeng Shang, Hongjun Lin (Author) https://creativecommons.org/licenses/by/4.0
An Effective Requests-Based Charging Station Scheduling and Routing Model with HUI-MTA ANN and BD-SSO for EV Charging
Electric Vehicles (EVs) rely on charging stations (CSs), but uncoordinated charging often leads to grid voltage deviations and long waiting times. This study proposes an integrated scheduling and routing framework that combines renewable-powered CSs with intelligent decision-making. A Singleton Membership Modified Fuzzy Logic Controller (SMM-FLC)-based MPPT ensures stable renewable power extraction, while a He-Uniform Initialized ModTanh Activated ANN (HUI-MTA-ANN) selects the optimal CS by considering queue lengths, travel deadlines, and cost. The Bayes Distributed Squirrel Search Optimization (BD-SSO) algorithm then identifies the most efficient route, and Quadratic Programming (QP) schedules charging slots. Results on an EV charging dataset show the framework achieves 97% accuracy with very low false selection rates, while significantly improving renewable energy utilization. The findings highlight a scalable and intelligent solution for reliable EV charging infrastructure.
Bayes distributed-Squirrel Search Optimization algorithm (BD-SSO)
He-Uniform Initialized-ModTanh Activated-Artificial Neural Network (HUI-MTA-ANN)
Maximum Power-Point-Tracking (MPPT) controller
Singleton Membership Modified Fuzzy Logic Controller (SMM-FLC)
Copyright (c) 2024 Rajat Yadav, Kalpana Chauhan, Rajeev Kumar Chauhan (Author) https://creativecommons.org/licenses/by/4.0
Solar Dish-Stirling Pump for Irrigation: Site Suitability and Performance Assessment
The growing global population has intensified demand for water, food, and energy, straining fossil fuel reserves and accelerating climate change. Water scarcity, especially in developing countries, hinders sustainable development. Enhancing agricultural productivity through modern irrigation technologies is essential to reducing rain-fed dependence and ensuring food security. Solar energy offers a sustainable alternative for irrigation. This study evaluates a solar dish concentrator integrated with a Stirling engine- driven pump and conical cavity receiver. The system, featuring a 2.8 m aperture diameter and 0.4 m depth concentrator, was assessed across four Ethiopian sites (Ziway, Dangila, Itang, and Jigjiga) based on solar radiation, groundwater potential, and irrigation demand. Using a weighted matrix method, Ziway was identified as the most suitable site for detailed analysis. COMSOL Multiphysics simulation showed an average receiver surface temperature of 574 K. Results indicate that solar radiation variations significantly affect thermal output, dish efficiency, and flow rate. Under optimal conditions, concentrator and Stirling engine efficiencies reached 83.69% and 39.01%, with overall energy and exergy efficiencies of 23.38% and 25.07%. The system produced 132 W peak power and 24,240 L/day flow, confirming potential for sustainable irrigation and rural development.
Copyright (c) 2024 Tesfaye Abera Tesema, Venkata Ramayya Ancha, Endeshaw Alemu Bekele, Ephrem Assefa Feyissa (Author) https://creativecommons.org/licenses/by/4.0
Steam Gasification of Biochar Obtained from Oil Palm Empty Fruit Bunches Using Peridotite as Catalytic Bed Material
Oil palm empty fruit bunch (OPEFB) is considered a recalcitrant agricultural waste due to the hard-to-digest structure of lignin. In this study, peridotite, a waste rock from the mining industry, was valorized as a suitable and cheap bed material for the gasification of OPEFB biochar. Besides, the effectiveness of three kinetic models on the representation of gasification was investigated. The gasification experiments were carried out in a stainless-steel fixed bed reactor at three temperatures: 700◦C, 760◦C, and 830◦C, using sand and calcined peridotite as bed materials. The experimental data were fitted using three kinetic models: volume reaction model (VRM), grain model (GM), and random pore model (RPM). The calcination of peridotite caused structural and surface changes in the material, such as sintering and the formation of ????-Fe2O3 and forsterite (Mg2SiO4). The maximum biochar conversions were higher with peridotite in comparison to sand. The predictive capacity of the tested models followed this order: RPM>VRM>GM. The activation energies (Ea) obtained with all models were lower for the gasifications with peridotite. The results proved a significant catalytic effect of peridotite for the OPEFB gasification in comparison to sand.
Copyright (c) 2025 David A. Echeverri, Otto A. Ortiz, Helen C. Inciarte, Luis A. Rios (Author) https://creativecommons.org/licenses/by/4.0
Assessment of Techno-Economic Hydro and Solar Power Energy Potential in Rural Region of Kulfo Catchment, Rift Valley Ethiopia
Eighty-five percent of Ethiopia’s population lives in rural areas, where only 36% have access to electricity. This study aimed to assess the techno-economic potential of hydro and solar power for rural regions in the Kulfo catchment using the Hybrid Optimization Model for Multiple Energy Resources (HOMER). Data from hydrologic, meteorological, and ERA5 reanalysis sources, along with household surveys, were used to analyze electricity demand. The selected sites, including T’sayte, Wisamo, Mayzo Doysa, and Gatse, were evaluated for run-of-the-river micro-hydropower and solar hybrid system potential was selected for this study. The HBV-IHMS model estimated stream flow at the sites, with Nash-Sutcliffe efficiency (NSE) of 0.69 for calibration and 0.63 for validation. Energy use in the selected areas was 19.77%, while 80.2% of energy went unused. The annual power production from solar photovoltaic (PV), hydropower, and generators was 5,641,903 kWh, 27,566,199 kWh, and 11,736,523 kWh, covering 12.6%, 61.3%, and 26.1% of the energy needs, respectively. The levelized cost of energy (LCOE) was 0.0962 dollars per kWh, slightly higher than the Ethiopian Electricity Corporation’s cost of 0.06 dollars per kWh.
Copyright (c) 2025 Mesay Feleke Negash, Getachew Bereta Geremew, Melkamu Teshome Ayana, Otoma Orkaido Garo (Author) https://creativecommons.org/licenses/by/4.0
A Critical Review of Development, Challenges and Techno-Economical Impacts of Floating Solar PV Systems: Bangladesh Case Study
The global shift towards land-efficient and decarbonized energy systems has driven innovation in floating solar photovoltaic (FPV) technologies. Best suited for water-surplus and land-scarce nations like Bangladesh, FPV offers a sustainable alternative to ground-mounted PV. This review critically evaluates FPV’s global development, technical efficiency, and environmental impact, with particular emphasis on the energy landscape of Bangladesh. Using solar irradiance data from NASA POWER, inland water surface availability from national hydrographic surveys, and performance benchmarks from FPV deployments in India and China, this review estimates that FPV could meet 15−20% of Bangladesh’s national electricity demand and offset up to 10 million tons of CO2 annually. The levelized cost of energy (LCOE) of FPV is marginally lower than that of land-based systems (0.07 vs 0.08 USD kWh−1) under equivalent assumptions of 20-year life, 6% WACC, and 0.5% yr−1 degradation, owing to reduced O&M and higher yield. Additional lifecycle environmental assessments (LCA) are proposed to provide quantitative long-term assessments of material acquisition, deployment, and end-of-life disposal costs for FPV systems. Integration into the grid also seems to be a significant issue, particularly with respect to intermittency, grid flexibility, and near-inland water body transmission infrastructure. Hybridization possibilities with hydropower are addressed herein, and technical, regulatory, and policy frameworks are proposed for implementation. The study aims to guide researchers, investors, and policymakers on how to apply FPV as a revolutionizing tool towards sustainable energy growth in Bangladesh and other delta regions.
Copyright (c) 2025 Abu Talha Haque Miah, Roby Mohajon, Sabuj Ahmed, A.B.M. Noushad Bhuiyan, Nur Mohammad (Author) https://creativecommons.org/licenses/by/4.0
AI-Based Lifespan Estimation and Energy Efficiency Optimization of Port Ship Unloaders Motors Under Harsh Environmental Conditions
Electric motors in port ship unloaders operate under harsh environmental conditions that accelerate degradation and increase energy consumption. This study develops a unified hybrid predictive maintenance framework that integrates an adaptive Extended Kalman Filter (EKF), a Long Short-Term Memory (LSTM) network, and Deep Reinforcement Learning (DRL). The EKF—enhanced with the adaptive Sage–Husa algorithm—serves as a dynamic state estimation and noise-mitigation module, improving the quality of multi-sensor data before learning and decision-making. The LSTM component captures nonlinear temporal degradation patterns, while the DRL agent learns optimal maintenance policies that balance reliability, operational safety, and energy efficiency. The proposed EKF–LSTM–DRL framework was validated on real and simulated datasets from Neuero ship unloader motors at Bandar Imam Khomeini Port (Iran). Results show a 20% reduction in Mean Absolute Error (MAE) and a 15% decrease in unplanned downtime compared with baseline models such as GRU and CNN. The main contributions of this work are: (1) a clear integration of adaptive EKF-based preprocessing with deep learning and reinforcement learning for robust Remaining Useful Life (RUL) prediction under maritime uncertainty; and (2) a dual optimization of predictive accuracy and energy efficiency. A detailed workflow diagram has been added to clarify the complete process and ensure conceptual transparency.
Copyright (c) 2024 Mohammad Ali Zarghami, Sadigh Raissi, Hamid Tohidi, Shahrooz Bamdad (Author) https://creativecommons.org/licenses/by/4.0
Analysis and Optimization of PV-Solar Chimney’s Hybrid System for Pleasant Building Ventilations
In this day and age considering the depletion of fossil fuels in the world in the upcoming decades and also the case of globalization and the increasing rivalry between countries of the world and international requirements for decreasing the creation of polluting gases (specially Greenhouse gas) has led to the optimization of energy usage, to be established as a strategic policy by economists and government officials worldwide. In this research, the effect of using a Solar chimney with the Photovoltaic system on building ventilations, and the relationship between each geometric dimensions of this system with the ventilated amount, have been studied. In the end it has been established that with the area reduction of the solar chimney's outfall, the speed around the outfall area increases. Which results in the reduction of pressure in chimney's outfall, increase of the ventilated air's rate and reduction of temperature. It has also been revealed that with the increase of Chimney's height for around 20cm, the amount of pressure difference is raised by 9% and the amount of temperature difference is decreased by 10%. The results suggest that the designed ventilation system of this research has performed the best in warm and dry climates. Also in order to examine the verification, the resulted numbers have been studied with the Laboratory's experimental data which resulted in high accommodation.
Copyright (c) -1 Mahdi Eslami, Alireza Saraei, Abazar Vahdat Azad, Mohammad Hassan Jalalodin Abyaneh (Author) https://creativecommons.org/licenses/by/4.0
PCM Phase Change Materials and Their Role in Reducing Residential Building Energy Consumption
Attention to phase change materials in architecture as one of the new ways to reduce energy consumption has increased significantly worldwide. The aim of this article is to study the effects of using these materials and to determine the optimal conditions for using PCM in Tehran by simulating energy consumption. In this article, the types of phase change materials were studied and then the performance of a sample of them was simulated under different conditions (thickness and location) in a sample building in Tehran city to determine the optimal conditions for using PCM. The optimum extent of energy consumption reduction occurs at low thicknesses of the phase change materials. As the thickness of PCM layer increases, the slope of the energy consumption reduction curve decreases, which is not economical due to the linear increase in the cost of using these materials. The best performance in reducing annual energy consumption by simulating energy consumption on an existing plan in Tehran is related to the east wall. Of course, if we simulate the performance of these materials in different walls by removing the interior walls and considering the entire building as one space with the same use, we will find that in this case the best performance is associated with the north wall, with a slight difference in the east and west walls.
Reducing residential building energy consumption
Copyright (c) 2025 Khalil Salehiparvar, Farivar Fazelpour, Ahmad Khoshgard (Author) https://creativecommons.org/licenses/by/4.0
Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring
Abstract The unexpected failure of wind turbine components leads to significant downtime and loss of revenue. To prevent this, supervisory control and data acquisition (SCADA) based condition monitoring is considered as a cost-effective approach. In several studies, the wind turbine power curve has been used as a critical indicator for power performance assessment. In contrast, the application of the blade pitch angle curve has hardly been explored for wind turbine condition monitoring purposes. The blade pitch angle curve describes the nonlinear relationship between pitch angle and hub height wind speed and can be used for the detection of faults. A support vector machine (SVM) is an improved version of an artificial neural networks (ANN) and is widely used for classification- and regression-related problems. Support vector regression is a data-driven approach based on statistical learning theory and a structural risk minimization principle which provides useful nonlinear system modeling. In this paper, a support vector regression (a nonparametric machine learning approach)-based pitch curve is presented and its application to anomaly detection explored for wind turbine condition monitoring. A radial basis function (RBF) was used as the kernel function for effective SVR blade pitch curve modeling. This approach is then compared with a binned pitch curve in the identification of operational anomalies. The paper will outline the advantages and limitations of these techniques.
Fixed-frequency implementation of sliding-mode controllers for photovoltaic systems
Abstract Traditional implementation of sliding-mode controllers, based on hysteresis comparators, exhibits variable switching frequency. Such a condition makes it difficult to select the semiconductor devices, design the passive elements of dc/dc converters, and design the sensing filters usually adopted for removing the switching noise from currents and voltages. Those problems are always present in photovoltaic systems based on variable-frequency sliding-mode controllers. Therefore, this paper proposes an implementation methodology for sliding-mode controllers providing constant switching frequency, which is aimed for grid-connected photovoltaic applications. The proposed solution does not compromise the power production of the PV system, hence it provides the same PV power in comparison with the classical variable-frequency implementation. Finally, the performance and correct operation of the fixed-frequency implementation are demonstrated using both simulation and experimental results.
Decoupled inputs sliding mode controllers for a fuel cell-supercapacitor module in hybrid generation applications
Abstract The development of Multiple Input/Multiple Output (MIMO) sliding mode control setups for a fuel cell/supercapacitor module is presented in this paper. The main objective of the proposed controllers consists in simultaneously satisfying the demand and regulating the DC bus voltage, even in the presence of model uncertainties and strongly varying operating conditions. Two design approaches are utilized to synthetise different control setups, each one capable to robustly deal with such control challenges: on one hand, variable-gains first-order sliding mode and, on the other, supert-wisting second-order sliding mode control. The stability of the nonlinear controlled system is formally analysed. Extensive simulations are conducted, to comparatively assess the performance of the proposed MIMO sliding mode controllers. Both control setups exhibited highly satisfactory results, demonstrating robustness to external disturbances and parameter variations, proving to be more suitable than classic linear PID controllers.
Air gasification of digestate and its co-gasification with residual biomass in a pilot scale rotary kiln
Abstract In this study energy recovery of digestate from a biogas plant was investigated via air gasification. Gasification tests were executed in a pilot scale rotary kiln plant having a nominal biomass feeding rate of about 20 kg/h. The equivalence ratio was varied from 0.22 to 0.39 with the goal to approach the autothermal condition. Tests were carried out for 5 h in steady state condition. Syngas composition, char and gas yields were measured. To improve the cold gas efficiency of the process, a mixture of digestate and almond shells (60:40 wt%) was gasified. Autothermal condition was reached with the mixture using equivalence ratio of 0.30 where the corresponding cold gas efficiency achieved the maximum value of 55%. The raw gas had a lower heating value of 4–5 MJ/Nm 3 . To evaluate possible improvements in the produced gas properties, in this work the effect of steam injection was also investigated.
The characteristics of heat inside a parked car as energy source for thermoelectric generators
Abstract Under a condition of sunlight exposure, the temperature of a parked car’s cabin will increase gradually. Compared to the ambient temperature, the inside and outside temperature will be different, which will be favourable for thermoelectric generators. This study investigated the temperature distribution pattern of a car parked in the direct sun using real and model cars. Performing different scenarios, we found that the car model equipped with a fan and a dimmer was the suitable approach to the real car model. Compared to the ambient temperature, results for both the real car and the model car indicated that heat rose drastically inside the car cabin. The calculation using the thermoelectric generator eTEG HV37 considerably could generate up to 6.9 mW per module. Therefore, by having a thermoelectric generator installed on an area of 1 m 2 of the car roof, 280 W power could be generated.
Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region
Abstract A 14-year-long data set containing daily values of meteorological variables was used to train three artificial neural networks (ANNs) for daily, weekly averaged and monthly averaged global solar radiation prediction for Fortaleza, in the Brazilian Northeast region. Local climate is semiarid coastal. Day of the year, maximum temperature, minimum temperature, irradiance, precipitation, cloudiness, extraterrestrial radiation, relative humidity, evaporation and wind speed were adopted as predictors. The ANNs were developed by an in-house code and trained with the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. Besides the lack of explicit predictors able to model El Niño and La Niña phenomena, which have strong influence on local weather, the accuracy of the predictions was considered excellent according to its values of normalized root-mean-square error (nRMSE) and good relative to mean absolute percentage error (MAPE) values. Both error metrics presented the smallest values for the monthly case study.
Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm
Ecological Footprint as a sustainability indicator to analyze energy consumption in a Portuguese textile facility
Abstract The main objective of this paper is to apply Ecological Footprint methodology as a tool to analyze the Portuguese textile industry. This analysis is related to energy and resources consumption in dyeing, printing and finishing processes. It also includes identification of mitigation measures to increase energy efficiency, promoting at the same time the reduction of CO 2 emissions and consequently the decrease in industry’s Ecological Footprint. Moreover, the analysis presents the Ecological Footprint reduction potential for each mitigation option proposed for an important textile facility located in the North Region of Portugal, used as a case study in this work. The results for the case study showed that the total Ecological Footprint accounted, for the year 2016, summed up to 4890 gha. Furthermore, the energy category corresponds to more than 50% of the total Ecological Footprint estimation, and natural gas was the most required fuel for the processes. The calculation of the Ecological Footprint in the textile facilities can be used as a tool to analyze the processes’ energy efficiency and to quantify the global impact of the implementation of mitigation measures in the energy field. The result is an indicator shown in a common unit, gha (global hectare) and it can be easily used to compare energy-intensive processes and industrial sectors. In addition, it can be used to analyze how each sector can impact the country’s total demand for resources.
Energy-saving behaviour as a demand-side management strategy in the developing world: the case of Bangladesh
Abstract Although demand-side management (DSM) needs to be more customer centred, either with or without smart technologies (e.g. smart grid), less attention has been paid to the developing world in relation to DSM strategy development. The main reasons have been lack of appropriate technology and capital costs. Importantly, there are alternative DSM strategies that require minimum or no cost to implement and provide immediate results, of which energy-saving behaviour of the occupants at residences is one. This study explores the potentiality of this energy-saving behaviour as a DSM strategy for the least developed economies, focusing particularly on Bangladesh. The literature suggests that energy-saving behaviour could reduce energy demand by a maximum of 21.9%. However, this potential DSM scheme seems underestimated in the national DSM programme of Bangladesh. The Energy Efficiency and Conservation Master Plan (EECMP) of Bangladesh (a DSM program) shows that efficiency improvement in the use of home appliances could reduce electricity demand in the residential sector by about 28.8%, but this does require a long time to be implemented, whereas the inclusion of energy-saving behaviour as a demand response strategy in residences along with the EECMP might achieve demand reduction of up to 50.7%. Although the findings from this study are specific to Bangladesh, these could be useful guidelines for the policymakers of other developing nations where national DSM strategy development is underway.
Developing a durable thermally insulated roof slab system using bamboo insulation panels
Abstract Traditional roofs can be effectively substituted by reinforced concrete roof slabs while gaining multiple advantages such as cyclonic resistance, possibility of future vertical extension and possibility of utilizing as an extra working space or a rooftop garden. Further, it adds a significant economic benefit from land regaining. However, the immediate space beneath the roof slab results in thermal discomfort and hence the inventions related to insulated roof slab systems have been increased recently. Although the expected thermal comfort could be achieved, most of the inventions use artificial thermal insulation materials such as polystyrene. This paper introduces a novel roof slab insulation system which uses the natural material of transversely cut bamboo layer as the thermal insulator. The proposed system minimizes the negative environmental impacts induced by the use of artificial insulation materials. The optimum insulation layer thickness is found to be 25 mm, which has acquired a 53% peak heat gain reduction with a decrement factor of 0.61 and a 3-h time lag.
Effects of butanol–gasoline blends on SI engine performance, fuel consumption, and emission characteristics at partial engine speeds
Abstract The effects of using butanol–gasoline-blended fuels on performance, fuel consumption, and emission characteristics of a four-cylinder spark-ignition engine were experimentally investigated. The butanol-blending fraction was varied from 10 to 50% by volume. The engine speeds were tested at 2250 and 4250 rpm, while the throttle positions were set at 30% and 70%. The engine performance, specific fuel consumption, and emission properties have been carried out and compared. The results show that, at high throttle position, the flame propagation speed of combustion process as using the butanol–gasoline blends decreases as increasing the butanol-blending fraction and this becomes more obvious with the increase of engine speed. The engine brake torque and power are improved, as the butanol-blending fraction is less than 30% at low open throttle position, while those are gradually decayed as increasing throttle opening level. A significant reduction is observed in specific fuel consumption, as the butanol-blending fraction is less than 30% for all the tests. The emissions of CO, HC, and CO 2 in the case of using butanol–gasoline blends are much better than those in the case of using pure gasoline. However, NOx emission is worse than that of the pure gasoline for all the test blends.
Characteristics of wood pellets mixed with torrefied rice straw as a biomass fuel
Abstract Utilization of rice straw as an alternative fuel source to fossil fuel has been considered. However, properties such as water content, low heating value, less grindability, high bulk density, and high ash content are problems in such a biomass fuel supply chain. This study attempted to produce wood pellets mixed with torrefied rice straw (WPTRS) to solve the problems related to using rice straw as fuel. For that, preferred torrefaction conditions, such as torrefaction temperature and holding time, were determined by indicators of the grindability, energy loss, and heating value. As a result, the preferred grindability was found at torrefaction temperatures of over 220 °C. In terms of energy, we derived two viewpoints: i.e., if minimum energy loss is prioritized, the preferred torrefaction temperature is 220 °C, and for an increase in the heating value of rice straw, 280 °C was preferred. Next, WPTRS was produced using rice straw torrefied at 220 °C and 280 °C, and by changing the mixing ratio of rice straw to wood. Burning tests were conducted to clarify the WPTRS characteristics and the results revealed that the torrefaction at a temperature of 280 °C increased WPTRS’s heating value. In addition, the basicity can be used as a good indicator for clinker production when wood and rice straw are used as fuel.
Wood pellets mixed with torrefied rice straw
A review on heat and mass integration techniques for energy and material minimization during CO2 capture
Abstract One major challenge confronting absorptive CO 2 capture is its high energy requirement, especially during stripping and sorbent regeneration. To proffer solution to this challenge, heat and mass integration which has been identified as a propitious method to minimize energy and material consumption in many industrial applications has been proposed for application during CO 2 capture. However, only a few review articles on this important field are available in open literature especially for carbon capture, storage and utilization studies. In this article, a review of recent progress on heat and mass integration for energy and material minimization during CO 2 capture which brings to light what has been accomplished till date and the future outlook from an industrial point of view is presented. The review elucidates the potential of heat and mass exchanger networks for energy and resource minimization in CO 2 capture tasks. Furthermore, recent developments in research on the use of heat and mass exchanger networks for energy and material minimization are highlighted. Finally, a critical assessment of the current status of research in this area is presented and future research topics are suggested. Information provided in this review could be beneficial to researchers and stakeholders working in the field of energy exploration and exploitation, environmental engineering and resource utilization processes as well as those doing a process synthesis-inclined research.
Effect of 21 August 2017 solar eclipse on surface-level irradiance and ambient temperature
Abstract The temporal variation in the direct normal irradiance, diffuse horizontal irradiance, global horizontal irradiance, and ambient temperature was quantified during the 21 August 2017 solar eclipse. The magnitude and obscuration of the eclipse at the observation location were 0.97 and 96.94%, respectively. A two-axis sun tracker equipped with a pyrheliometer, a shaded pyranometer, an unshaded pyranometer, and a pyrgeometer were utilized to obtain the solar irradiance measurements. Thermistors contained within the pyrheliometer and the pyrgeometer were used to quantify changes in temperature. Unfortunately, on the day of the eclipse, the weather conditions were not ideal and periodic cloud coverage impacted measurements of the direct normal irradiance. As such, solar irradiance and temperature data from 2 days prior, when the weather conditions were near-ideal, are presented for comparison. The temperature of the air as measured by the thermistors enclosed within the pyrheliometer and pyrgeometer recorded an average temperature drop of 8.49 °C. The minimum temperature was recorded approximately 47 min after totality. The diffuse horizontal irradiance reached a maximum value of 495.9 W/m 2 shortly after the start of partial eclipse and dropped to a minimum value of 0 W/m 2 near totality. Similarly, the global horizontal irradiance reached a maximum value of 1138 W/m 2 and dropped to a minimum value of 1.54 W/m 2 near totality. For reference, at the same time of day, but 2 days prior, the diffuse horizontal irradiance and global horizontal irradiance were at values of 89.2 W/m 2 and 962 W/m 2 , respectively, under clear sky conditions. The rate of decrease in the temperature of the air as well as in the diffuse and global horizontal irradiances after first contact and before totality was greater than the rate of increase in these parameters after totality. The difference in the magnitude of these gradients before and after totality is largely attributed to normal diurnal variations as the solar eclipse occurred in the afternoon.
Abstract More than 60% of household energy consumption in Ontario is for heating. Home heating needs in Ontario are driven by exterior temperatures that fluctuate throughout the day. Ontario’s electricity is generated from a different mix of primary energy sources from hour to hour. Using average hourly data for the electricity generation mix and hourly outside temperature data for each month of the year, we estimate residential heating loads and the electricity demands due to the use of three models of heat pump. Then we calculate the resultant greenhouse gas emissions and compare them to emissions if heat pumps are not used. We determine heating needs of single detached dwellings using prototypical average Ontario homes and building simulation software. Using heat pumps in all of these dwellings can reduce heating-related greenhouse gas emissions between 15% and 85% during January, the harshest month of the year. Using heat pumps could also reduce energy consumption for heating by between 12% and 68%, while requiring an approximate 5–25% increase in electricity demand. Heat pumps can provide a significant portion of home heat needs whilst reducing energy consumption and greenhouse gas emissions. Operating costs are lower than that of electric and oil heating, but similar to natural gas heating.
Short-term nonlinear autoregressive photovoltaic power forecasting using statistical learning approaches and in-situ observations
Abstract Due to the low total cost of production, Photovoltaic energy constitutes an important part of the renewable energy installed in the world. However, photovoltaic energy is volatile in nature because it depends on weather conditions, which makes the integration, control and exploitation of this type of energy difficult for grid operators. In the traditional grid architecture, system operators have accumulated enough experience that enables them to determine how much operating reserves are required to maintain system reliability based on statistical tools. Still, with the introduction of renewable energy (wind and photovoltaic), the grid structure has changed, and to maintain grid stability, it is becoming fundamental to know renewable energy state and production that can be combined with other less variable and more predictable sources to satisfy the energy demand. Therefore, renewable energy forecasting is a straightforward way to integrate safely this kind of energy into the current electric grid, especially photovoltaic power forecasting, which is still at a relative infancy stage compared to wind power forecasting, which has reached a relatively mature stage. The goal of this work is to present, first, a short-term offline forecasting model that uses only in-situ (local) collected data. Also, the performances of several pure non-linear auto-regressive models are investigated against those of non-linear auto-regressive models with exogenous inputs. For this purpose, two well-known statistical learning techniques, namely Feed Forward Neural Network and Least Square Support Vector Regression, have been used. To test the performance of the models, the results obtained are compared with those of a benchmark model. In this paper, we used the persistent model as well as a multivariate polynomial regression model as benchmark.
Gas escape to crankcase: impact of system parameters on sealing behavior of a piston cylinder ring pack
Abstract Internal combustion engines are the generators of energy for many transportation applications, but they still have an overall low efficiency due to mechanical and thermal losses. The combustion chamber is the core element of the engine and it ought to be perfectly sealed; however, some of the gas leaks toward the crankcase due to imperfect sealing of the rings. This leakage is known as blow-by and affects efficiency, correct lubrication and emissions. The aim of this paper was to understand, in a more detailed way, how some parameters could affect the sealing efficiency of a ring pack. In particular, ring gaps, ring masses and elastic properties, and ring static twists, were varied from the original and investigated for their influence on the inter-ring dynamics and sealing efficiency. The problem, referred to a turbo diesel engine, was formulated in terms of motion equations for the rings and gas equations for the inter-ring crevices, and solved in © Ricardo RINGPAK solver. The results were compared with the original design and with the reference literature. These results confirm that ring gaps and ring unstable motion have an important role in the phenomenon of gas blow-by. In addition, the second ring emerged to have a more important role on the blow-by reduction with respect to the top ring. However, this phenomenon is complex due to the interaction of several parameters, not all of which were included in this study. Nevertheless, these findings can already be taken into account for further studies or experimental investigations.
Air pollution effects on the intensity of received signal in 3G/4G mobile terminal
Abstract In this paper, the existence of the interaction between the atmospheric particulate and the electromagnetic waves attenuation in the frequency band used by the 3G/4G mobile network was investigated. A few papers present in literature indicate, in presence of air pollution, the possibility of a significant cross polarization of the electromagnetic waves, while none of the papers indicated the possibility of a significant attenuation. This paper has tried to partially fill this gap of knowledge in this field with some new contributions. In all the experiments performed at the department of electrical, electronics and informatics engineering in the University of Catania (87 experiments each one lasting about 36 min), significant variations have not been found in the received signal level (RSL), while a polynomial dependency of the RSL local variance from the particulate concentration has been found. To demonstrate that the results obtained in the laboratory have general validity, a campaign test was conducted in an outdoors environment in the frequency band used by the 3G/4G mobile network. The site of the considered measurement station is selected to represent a typical residential area with high traffic influences in the city of Catania. Also, in this measures campaign conducted in the outdoors environment in the 3G/4G frequency band, significant variations have not been found in the RSL. While a marked dependence of the RSL local variance from the PM10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text {PM}}_{10}$$\end{document} concentration has been found. This is a noteworthy result, because certain non-negligible RSL local variance variations indicate the possibility of a pronounced cross polarization. The impact of the cross polarization becomes significant for systems reusing frequency, because the cross polarization counteracts the optimum frequency spectrum utilization.
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A prototype of a low-cost solar-grid utility hybrid load sharing system for agricultural DC loads
Abstract The main objective of this work is to design a prototype of a low-cost solar-grid utility hybrid load sharing system to support the agricultural DC equipment that has already been used in the rural area of Thailand. Time-division multiplexing (TDM) technique is employed in the prototype construction, and it is used to evaluate the time interval for two power MOSFETs to switch between two applicable power supplies: in this case, solar panels and grid utility. The time interval is generated by the internal timer from a microcontroller and is divided into two square waves which are 180° out of phase (opposite sides). Each time interval is derived by the fuzzy logic controller and is calculated based on the consumption of electrical load. The experimental results are divided into three parts; first result shows a smooth output level that is consisted of the signal between solar panels and grid utility for each particular ratio. The second result shows a high efficiency of this system upon the variation of load power. Finally, we demonstrate a relatively good sharing of the electrical power when this system is operated under various capacities of the power sources. Our prototype offers the feasibility of hybridization of renewable energy and grid utility in which the technology is easily accessible by agriculturists throughout the world.
Uncertainty and sensitivity analyses applied to a dynamic simulation of the carbon dioxide concentration in a detached house
Abstract This paper aims to study the variability of indoor CO 2 concentration due to occupant behaviour and physical parameter uncertainties. A case study, conducted in a mechanically ventilated detached house, is presented with an uncertainty and sensitivity analysis (Monte Carlo method with a Latin hypercube sampling). Uncertainties related to occupant behaviour are described by combining four types of scenarios: occupation, generation of CO 2 per person, indoor doors, and outdoor windows’ openings. The uncertainty analysis showed that despite an acceptable average room CO 2 concentration, large variations, due to input parameter uncertainties, are observed in CO 2 instantaneous concentrations. Moreover, during occupied periods, average value is relatively important (higher than 1300 ppm). Occupants spent around 30% of the time at CO 2 concentrations over 1500 ppm. Large output uncertainties are reached on the cumulative CO 2 concentration and time fraction spent over 1500 ppm. The sensitivity analysis highlights the strong influence of the parameters related to bedrooms (number of occupants, night generation of CO 2 ) and of the kitchen extracted airflow rate. It also shows that low-level air change rates in bedrooms are mainly caused by an incorrect air distribution in the building. Potential solutions to reduce both concentrations and uncertainties are discussed.
Evaluating solar energy technical and economic potential on rooftops in an urban setting: the city of Lethbridge, Canada
Abstract Solar energy deployment is gaining greater attention as a sustainable source of energy that could alleviate aspects of the current climate crisis. Knowledge of the characteristics and economics of the solar electricity sector is required to integrate it in the energy generation and utilization mix. Unlike energy generation from fossil fuels, renewable energy sources have relatively low geographic density and are spread unevenly over large areas. Therefore, especially in cities, where space has greater value and opportunity costs, finding suitable spaces for implementing solar systems are essential to promote the use of solar technologies. Using remote-sensing data, the intricate topography of cities can be modelled, and insolation incident at each location can be estimated. A multi-criteria approach based on geographic information systems (GIS) and light detection and ranging (LiDAR) is used in this research to estimate rooftop photovoltaic electricity potential of buildings in an urban environment, the city of Lethbridge. An economic assessment is conducted utilizing present market prices to determine economically attractive rooftop PV systems. The total rooftop photovoltaic (PV) electricity potential is evaluated and compared with the local electricity demand. Effective expansion of solar power systems in the city is achieved by determining the geographic distribution of the best locations for exploiting the systems. This study estimates that the rooftop PV electricity generation potential of the city of Lethbridge is approximately 301 ± 29 (SD) GWh annually (almost 38% of its annual electricity consumption in 2016), and about 96% of the recognized potential rooftop PV systems are economically feasible. The results can assist in making informed policy decisions about investment in deployment of renewable energy generation.
Operation management of a renewable microgrid supplying to a residential community under the effect of incentive-based demand response program
Abstract A micro-grid (MG) comprises different energy sources of different operational characteristics. In this paper, we present an operation management model of a MG integrated with small renewable energy resources. The MG is operating in grid-connected mode and feeding energy to a residential community. The consumers of the residential community are participating in an incentive-based demand response (DR) program. The problem is formulated for the generation scheduling of the MG sources so that the operational cost and pollutants emission from the MG could be minimized in the presence of incentive responsive loads. The problem is then solved by the mixed integer linear programing technique using CPLEX solver of the GAMS software. The simulation results are achieved by solving the problem under three different cases. For the comparison of the results, we consider two scenarios in each case; (i) without DR program, (ii) with DR program. Finally, the trade-off between two conflicting objectives has been analyzed and optimal solution is achieved and presented in the paper.