Performance and environmental sustainability studies on a dual-fuel IC engine working with producer gas of variable calorific values from rural biomass.

Decentralized power generation using renewable gaseous biofuels faces challenges due to their inconsistent quality and availability. Mixing producer gas (PG) with diesel as a secondary fuel is a promising option, but the non-stable calorific value (CV) of PG from different biomasses poses a serious problem for the efficient operation of a dual-fuel engine. This study aims to examine how the CV of PG from various biomasses affects a 3.75-kW dual-fuel IC engine's performance. The experimental facility, which has a dual-fuel engine and a 115-kW thermal gasifier, tested the blends of diesel and PG from different biomasses. We chose biomass based on its availability and PG's CV of 3.4, 4.4, 5.2, and 6.3 MJ/Nm3. For this range of CV, the efficiency, energy consumption, and fuel replacement of a dual-fuel engine vary between 20.9 and 26.6%, 17.3 and 13.5 MJ/kWh, and 10.8 and 76.9%, respectively. Furthermore, the blend with the maximum CV of PG had a 69.64% lower specific diesel consumption and an 86% higher diesel replacement rate than the blend with the lowest CV of PG. In terms of emission characteristics, the comparison showed a 2.02-7.06% reduction in NOx and a 4.05-55.6% increase in hydrocarbons (HCs) for the tested conditions. The overall observations demonstrated that a significant enhancement in a dual-fuel engine's performance is possible with a higher CV of PG. However, the emissions trade-offs demand additional optimization studies, as well as case-based research, in order to integrate renewable energy and emission management in smaller-scale applications across more geographies.

Multi-objective green vehicle scheduling problem considering time window and emission factors in ship block transportation.

Logistics distribution is one of the main sources of carbon dioxide emissions at present, and there are also such distribution problems in the shipbuilding process. With the increasing attention paid to environmental problems, how to effectively reduce the energy consumption of block transportation and improve the utilization rate of resources in the factory is the key problem that China's shipbuilding industry needs to solve at present. This article considers the time windows for block transportation tasks, as well as the self-loading constraints of different types of flat cars, and establishes an optimization model that minimizes the empty transport time and energy consumption of the flat cars as the optimization objective. Then, an Improved Genetic Whale Optimization Algorithm is designed, which combines the cross and mutation ideas of genetic algorithms and proposes a whale individual position updating mechanism under a mixed strategy. Furthermore, the performance and computational efficiency of the algorithm are verified through comparative analysis with other classical optimization algorithms on standard test examples. Finally, the shipyard's block transportation example proves that the energy-saving ship block transportation scheduling method can effectively improve the efficiency of shipbuilding enterprise's block transportation and reduce the energy consumption in the block transportation process. It proves the engineering practicality of the green dispatching method proposed in this paper, which can further provide a decision-making method for shipyard managers.

Combining Fleetwide AviTeam Aviation Emission Modeling with LCA Perspectives for an Alternative Fuel Impact Assessment.

Reducing aviation emissions is important as they contribute to air pollution and climate change. Several alternative aviation fuels that may reduce life cycle emissions have been proposed. Comparative life cycle assessments (LCAs) of fuels are useful for inspecting individual fuels, but systemwide analysis remains difficult. Thus, systematic properties like fleet composition, performance, or emissions and changes to them under alternative fuels can only be partially addressed in LCAs. By integrating the geospatial fuel and emission model, AviTeam, with LCA, we can assess the mitigation potential of a fleetwide use of alternative aviation fuels on 210 000 shorter haul flights. In an optimistic case, liquid hydrogen (LH2) and power-to-liquid fuels, when produced with renewable electricity, may reduce emissions by about 950 GgCO2eq when assessed with the GWP100 metric and including non-CO2 impacts for all flights considered. Mitigation potentials range from 44% on shorter flights to 56% on longer flights. Alternative aviation fuels' mitigation potential is limited because of short-lived climate forcings and additional fuel demand to accommodate LH2 fuel. Our results highlight the importance of integrating system models into LCAs and are of value to researchers and decision-makers engaged in climate change mitigation in the aviation and transport sectors.

Sustainability in maritime transport: Selecting ballast water treatment for a bulk carrier.

The study provides a comprehensive assessment of ballast water treatment systems (BWTS) selection regarding crucial parameters such as energy efficiency, fuel consumption, and CO2 emissions. The focus of the study is investigating the environmental impacts of BWTS and how these impacts can be considered in decision-making processes. In this context, it comprehensively analyzes the importance of decision-making parameters and the environmental consequences of BWTS applications. The potential impacts of the system on the sustainability of the maritime industry are highlighted. In this context, seven different BWTSs for a bulk carrier of 83,000 deadweight tonnes are evaluated using Analytical Hierarchy Process (AHP) and Preference Ranking Technique by Similarity to Ideal Solution (TOPSIS). According to the analysis, Operational Expenditure (0.314508), Capital Expenditure (0.249515), and Capacity (0.159952) are the most critical factors. Among the seven systems analysed, product G (0.8561137) emerges as the most suitable option. The analysis represents 3% of total emissions (456.5 tonnes CO2) and highlights the impact of BWTS on CO2 emissions. The results underline the need for innovative approaches aimed at ensuring the long-term sustainability of the maritime industry.

Design and performance evaluation of an off-grid hybrid solar system in a remote location: A case study of Legundi Island.

Legundi Island, located in Lampung Bay, Indonesia, has high solar energy potential but still relies on Diesel Power Plants. This research aims to design a hybrid Solar Power Plant and Diesel Power plant system that is environmentally friendly and reliable. Analysis was conducted using the Homer software. •This research designs a hybrid Solar Power Plant system with a capacity of 170.2 kWp and a battery of 1,036 kWh, integrated with three existing Diesel Power Plants units with a capacity of 200 kW.•This system can generate electricity of 296,391 kWh/year with a renewable energy fraction reaching 79.9% and an electricity surplus of 0.988%.•Fuel consumption decreased by 79.3% compared to the existing Diesel Power Plant system.

Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization.

This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton's equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU.

Fuel consumption rate and emissions variability in waste collection services routes: case study of Cascais Ambiente.

In the design waste collection systems, it is common practice to use a constant specific fuel consumption (e.g. litres per amount of waste collected or distance travelled). This is also the approach used in many cases for fleet management, namely, for decision-making on more fuel-efficient equipment acquisition. However, the specific fuel consumption is not constant and there are spatial and temporal variations. Accounting for this variability becomes relevant if a more refined cost or environmental optimization is intended. The present research effort evaluates the energy intensity of the waste collection service in the Cascais municipality, reporting the differences and the magnitude of the variability for the mixed waste collection service. Statistically significant differences are found between the circuits, the trucks, months of the year and days of the week. It is discussed that extrapolating average fuel consumption rates for decision-making in new waste collection systems may be prone to substantial error.

Environmental Impact of a Pediatric and Young Adult Virtual Medicine Program: A Lesson from the COVID-19 Pandemic.

OBJECTIVES: The Coronavirus Disease 2019 (COVID-19) pandemic led to the expansion of virtual medicine as a method to provide patient care. We aimed to determine the impact of pediatric and young adult virtual medicine use on fossil fuel consumption, greenhouse gas, and nongreenhouse traffic-related air pollutant emissions. METHODS: We conducted a retrospective analysis of all virtual medicine patients at a single quaternary-care children's hospital with a geocoded address in the Commonwealth of Massachusetts prior to (March 16, 2019-March 15, 2020) and during the COVID-19 pandemic (March 16, 2020-March 15, 2021). Primary outcomes included patient travel distance, gasoline consumption, carbon dioxide and fine particulate matter emissions as well as savings in main hospital energy use. RESULTS: There were 3,846 and 307,273 virtual visits performed with valid Massachusetts geocoded addresses prior to and during the COVID-19 pandemic, respectively. During 1 year of the pandemic, virtual medicine services resulted in a total reduction of 620,231 gallons of fossil fuel use and $1,620,002 avoided expenditure as well as 5,492.9 metric tons of carbon dioxide and 186.3 kg of fine particulate matter emitted. There were 3.1 million fewer kilowatt hours used by the hospital intrapandemic compared to the year prior. Accounting for equipment emissions, the combined intrapandemic emission reductions are equivalent to the electricity required by 1,234 homes for 1 year. CONCLUSIONS: Widespread pediatric institutional use of virtual medicine provided environmental benefits. The true potential of virtual medicine for decreasing the environmental footprint of health care lies in scaling this mode of care to patient groups across the state and nation when medically feasible.

Life cycle assessment of electric vehicles: a systematic review of literature.

This study addresses the pressing need to evaluate the life cycle assessment (LCA) of electric vehicles (EVs) in comparison to traditional vehicles, amid growing environmental concerns and the quest for sustainable transportation alternatives. Through a systematic four-stage literature review, it strives to provide essential insights into the environmental impact, energy consumption, and resource utilization associated with EVs, thereby informing well-informed decisions in the transition to more sustainable transportation systems. The study's findings underscore a compelling environmental advantage of EVs. They emit a staggering 97% less CO2 equivalent emissions when compared to petrol vehicles, and a significant 70% less compared to their diesel counterparts, rendering them a crucial instrument in the battle against climate change. These environmental benefits are intricately linked to the adoption of clean energy sources and advanced battery technology. Furthermore, the study highlights the potential for additional emissions reduction through the extension of EV lifespans achieved by recycling and advanced battery technologies, with Li-ion batteries enjoying a second life as secondary storage systems. However, challenges remain, most notably the scarcity of rare earth materials essential for EV technology. The study's policy recommendations advocate for a swift shift towards clean energy sources in both EV production and usage, substantial investments in advanced battery technology, and robust support for recycling initiatives. Addressing the rare earth material shortage is paramount to the sustained growth and viability of EVs, facilitating a greener and more sustainable future in the realm of transportation.

Determinants of the renewable energy consumption: The case of Asian countries.

In recent year, Asia has rapid economic growth and policy makers care about the renewable energy consumption. The motivation about the choice of variables with theory and empirical backing on the subject in the manuscript is the theory of Cobb- Douglas function and determinants of renewable energy consumption in the Asian countries. This paper investigates the nexus of electricity consumption, fossil fuel consumption, foreign direct investment inflows, economic growth, and renewable energy consumption in Asian countries. Utilizing panel data analysis, the study finds significant interrelationships among these factors and highlights their implications for energy policy and sustainable development in the region. This article aims to use the Cobb- Douglas Model to analyze the factors affecting renewable energy consumption at Asian developed countries for sustainable development goals. This study collected data from World Bank in the period 2000-2020 of 06 developed countries such as Japan, Korea, Singapore, Hong Kong, Israel and China. The empirical research results show that the electricity consumption, fossil fuel consumption, foreign direct investment inflows, economic growth affect the environmental pollution in 06 Asian developed countries. The electricity consumption, foreign direct investment inflows, economic growth positively affect to the renewable energy consumption. If the electricity consumption increases 1 % then the renewable energy consumption up 0.6 %; if the foreign direct investment inflows up 1 % then the renewable energy consumption up 0.82 %; If the economic growth up 1 % then renewable energy up 2.73 %. In addition, fossil fuel consumption negatively affects the renewable energy consumption. If the fossil fuel consumption increases 1 % then the renewable energy consumption down 0.26 %. The paper offers conclusions and recommendations for Asian countries to address these interconnected issues and transition towards a more sustainable energy future.

Time-varying impact of income and fossil fuel consumption on CO2 emissions in India.

This paper investigates the time-varying effects of fossil fuel consumption on CO2 emissions in India utilizing the time-varying cointegration test, allowing for multivariate long-run time-varying cointegration parameter developed by Bierens and Martins (2010) and the time-varying vector autoregressive (TVP-VAR) model developed by Primiceri (2005). The long-run time-varying coefficients reveal that GDP has a positive and increasing impact on CO2 emissions over time. Moreover, results confirm the polluting effects of all fossil fuels. Besides, the TVP-VAR model findings also demonstrate that changes in income and fossil fuel consumption have a positive and significant impact on environmental degradation. Coal is found to be the most polluting fuel, followed by oil consumption. Furthermore, the time-varying responses show that increased natural gas consumption has the least influence when compared to other fossil fuels on CO2 emissions.

Comparison of machine learning algorithms for predicting diesel/biodiesel/iso-pentanol blend engine performance and emissions.

In this study, machine learning techniques, namely artificial neural network (ANN), support vector machine (SVM), and extreme gradient boosting (XGBoost), were used to comprehensively evaluate engine performance and exhaust emissions for different fuel blends. To obtain valuable insights on optimizing engine performance and emissions for alternative fuel blends and thus contribute to the advancement of knowledge in this field, we focused on iso-pentanol ratios while maintaining the biodiesel ratios constant. The maximum brake thermal efficiency (BTE) values for the diesel (30.13 %), D85B10P5 (29.92 %), D80B10P10 (29.89 %), and D70B10P20 (29.79 %) blends were achieved at 1600 rpm. At 1600 rpm, the brake-specific fuel consumption (BSFC) values for the diesel, D85B10P5, D80B10P10, and D70B10P20 blends were 189.93, 200.93, 202.93, and 203.95 g kWh-1, respectively. In engine performance prediction, the ANN model exhibited superior performance, yielding regression coefficient (R2), root mean square error, and mean absolute error values of 0.984, 0.411 %, and 0.112 %, respectively, in BTE prediction, and 0.958 %, 6.9 %, and 2.95 %, respectively, in BSFC prediction. In exhaust gas temperature prediction, the SVM model exhibited the best performance, yielding an R2 value of 0.981. Although all models successfully predicted NOx emissions, the ANN model exhibited the best performance, achieving an R2 value of 0.959. In CO2 and hydrocarbon estimation, the XGBoost model exhibited the best performance, yielding R2 values of 0.956 and 0.973, respectively. Therefore, the ANN model can be used to accurately predict engine performance, and the XGBoost model can be used to accurately predict emission parameters.

Do industrialization and nonrenewable energy affect environmental quality? Evidence from top fossil fuel-consuming countries.

The global warming phenomenon has been an issue of considerable discussion and debate among academics and decision-makers over the past few decades. Therefore, a deeper comprehension of the relationships between environmental deterioration and its causes is necessary in nations that rely on fossil fuels. This study examines the relationship between per capita carbon dioxide emissions and total natural resources, nonrenewable energy, industrialization, and ecological footprint from 2001 to 2020 in the case of major fossil fuel-consuming countries. The most recent panel Granger causality and panel-corrected standard error (PCSE) simulation models are used in this study. The findings indicate that natural resources, ecological footprint, and registered companies impede environmental quality. Similarly, the same results were noted by employing the generalized least square method. A feedback effect was noted between carbon dioxide emission and ecological footprint, while unidirectional causality between coal consumption and carbon emission. In light of these findings, it is advised that instead of pursuing policies that encourage the use of coal and petroleum, new energy policies enhance the share of nonfossil fuels in the energy mix for controlling rapid industrialization, extraction of natural resources, and environmental and economic issues.

Utilization of additives in biodiesel blends for improving the diesel engine performance and minimizing emissions through a modified Taguchi approach.

Biodiesel from Jatropha oil is produced through catalyzed homogeneous transesterification. Hydrogen peroxide (H2O2) is considered as additive. Blends of Jatropha considered in the present study are 60% diesel, (40-A)% biodiesel and A% additive, varying A from 0 to 10. Identifying optimal input variables (such as additive volume percentage, injection pressure, and load) is important for improving the engine performance and reducing emissions. Air-fuel ratio; brake specific fuel consumption (BSFC); and brake thermal efficiency (BTE) are the engine performance characteristics. Carbon monoxide (CO); carbon dioxide (CO2); exhaust gas temperature (EGT); nitrogen oxide (NOx); and smoke opacity are the emission characteristics. 27 experiments need to be performed for the assigned 3 levels and 3 input variables. The Taguchi's L9 orthogonal array (OA) is chosen to perform only 9 experiments to obtain the optimal solution. The expected range of performance characteristics and emissions was obtained following a modified Taguchi approach. Empirical relationships are developed and verified through engine performance and emission characteristics.

Telematics data for geospatial and temporal mapping of urban mobility: Fuel consumption, and air pollutant and climate-forcing emissions of passenger cars.

In this study, we use the approach of geospatial and temporal (GeoST) mapping of urban mobility to evaluate the speed-time-acceleration profile (dynamic status) of passenger cars. We then use a pre-developed model, fleet composition and real-world emission factor (EF) datasets to translate vehicles dynamics status into real-urban fuel consumption (FC) and exhaustive (CO2 and NOx) emissions with high spatial (15 m) and temporal (2 h) resolutions. Road transport in the West Midlands, UK, for 2016 and 2018 is the spatial and temporal scope of this study. Our approach enables the analysis of the influence of factors such as road slope, non-rush/rush hour and weed days/weekends effects on the characteristics of the transport environment. The results show that real-urban NOx EFs reduced by more than 14 % for 2016-18. This can be attributed to the increasing contribution of Euro 6 vehicles by 63 %, and the increasing contribution of diesel vehicles by 13 %. However, the variations in the real-urban FC and CO2 EFs are less significant (±2 %). We found that the FC estimated for driving under the NEDC (National European Driving Cycle) is a qualified benchmark for evaluating real-urban FCs. Considering the role of road slope increases the estimated real-urban FC, and NOx, and CO2 EFs by a weighted average of 4.8 %, 3.9 %, and 3.0 %, respectively. Time of travel (non-rush/rush hour or weed days/weekends) has a profound effect on vehicle fuel consumption and related emissions, with EFs increasing in more free-flowing conditions.

Modeling fuel consumption and emissions by taxis in Tabriz, Iran: uncertainty and sensitivity analyses.

Taxis pose a higher threat to global climate change and human health through air emissions. However, the evidence on this topic is scarce, especially, in developing countries. Therefore, this study conducted estimation of fuel consumption (FC) and emission inventories on Tabriz taxi fleet (TTF), Iran. A structured questionnaire to obtain operational data of TTF, municipality organizations, and literature review were used as data sources. Then modeling was used to estimate fuel consumption ratio (FCR), emission factors (EFs), annual FC, and emissions of TTF using uncertainty analysis. Also, the impact of COVID-19 pandemic period was considered on the studied parameters. The results showed that TTF have high FCRs of 18.68 L/100 km (95% CI=17.67-19.69 L/100 km), which are not affected by age or mileage of taxis, significantly. The estimated EFs for TTF are higher than Euro standards, but the differences are not significant. However, it is critical as can be an indication of inefficiency of periodic regulatory technical inspection tests for TTF. COVID-19 pandemic caused significant decrease in annual total FC and emissions (9.03-15.6%), but significant increase in EFs of per-passenger-kilometer traveled (47.9-57.3%). Annual vehicle-kilometer-traveled by TTF and the estimated EFs for gasoline-compressed natural gas bi-fueled TTF are the main influential parameters in the variability of annual FC and emission levels. More studies on sustainable FC and emissions mitigation strategies are needed for TTF.

Optimizing ship speed depending on cargo and wind-sea conditions for sustainable blue growth and climate change mitigation.

The impact of fuel consumption on merchant ships is categorized in both economic and environmental ways in terms of sustainable blue growth. Apart from the economic benefits of reducing fuel consumption, attention should be paid to related environmental concerns with ship fuels. As a result of global regulations and agreements concerning mitigating greenhouse gases on board, such as the International Maritime Organization and Paris Agreement, ships have to take a step to reduce fuel consumption to adopt these regulations. The present study aims to determine optimal speed diversity depending on ships' cargo amounts and wind-sea states to reduce fuel consumption. Within this context, one-year voyage data from two model sister Ro-Ro cargo ships were used, including daily ship speed, daily fuel consumption, ballast water consumption, total ship cargo consumption, sea state, and wind state. The genetic algorithm method was used to determine the optimal diversity rate. In conclusion, after speed optimization, optimum speed result values are calculated between 16.59 and 17.29 knots; thus, approximately 18% of exhaust gas emissions were also reduced.

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors.

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver's health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle's central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver's heart condition and vehicular traffic have been found in this analysis.

Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning.

The time that a vehicle merges in a lane reduction can significantly affect passengers' safety, comfort, and energy consumption, which can, in turn, affect the global adoption of autonomous electric vehicles. In this regard, this paper analyzes how connected and automated vehicles should cooperatively drive to reduce energy consumption and improve traffic flow. Specifically, a model-free deep reinforcement learning approach is used to find the optimal driving behavior in the scenario in which two platoons are merging into one. Several metrics are analyzed, including the time of the merge, energy consumption, and jerk, etc. Numerical simulation results show that the proposed framework can reduce the energy consumed by up to 76.7%, and the average jerk can be decreased by up to 50%, all by only changing the cooperative merge behavior. The present findings are essential since reducing the jerk can decrease the longitudinal acceleration oscillations, enhance comfort and drivability, and improve the general acceptance of autonomous vehicle platooning as a new technology.

Transport infrastructure, economic growth, and transport CO2 emissions nexus: Does green energy consumption in the transport sector matter?

Attaining Sustainable Development Goals (SDGs) is important to control the adverse impacts of climate change and achieve sustainable development. Among the 17 SDGs, target 13 emphasizes enhancing urgent actions to combat climate-related changes. This target is also dependent on target 7, which advocates enhancing access to cheap alternative sustainable energy. To accomplish these targets, it is vital to curb the transport CO2 emissions (TCO2) which increased by approximately 80% from 1990 to 2019. Thus, this study assesses the role of transport renewable energy consumption (TRN) in TCO2 by taking into consideration transport fossil fuel consumption (TTF) and road infrastructure (RF) from 1970 to 2019 for the United States (US) with the intention to suggest some suitable mitigation policies. Also, this study assessed the presence of transport environmental Kuznets curve (EKC) to assess the direction of transport-induced growth. The study used the Bayer-Hanck cointegration test which utilizes four different cointegration techniques to decide cointegration along with the Gradual Shift causality test which considers structural shift and fractional integration in time series data. The long-run findings of the Dynamic Ordinary Least Squares (DOLS) test, which counters endogeneity and serial correlation, revealed that the transport renewable energy use mitigates as well as Granger causes TCO2. However, transport fossil fuel usage and road infrastructure enhance TCO2. Surprisingly, the transport EKC is invalid in the case of the US, and increased growth levels are harmful to the environment. The association between TCO2 and economic growth is similar to a U-shaped curve. The Spectral Causality test revealed the growth hypothesis regarding transport fossil fuel use and economic growth connection, which suggests that policymakers should be cautious while decreasing the usage of transport fossil fuels because it may hamper economic progress. These findings call for revisiting growth strategies and increasing green energy utilization in the transport sector to mitigate transport emissions.