Use of Gasoline, LPG and LPG-HHO Blend in SI Engine: A Comparative Performance for Emission Control and Sustainable Environment

Acetone-Gasoline Blend as an Alternative Fuel in SI Engines: A Novel Comparison of Performance, Emission, and Lube Oil Degradation

The disproportionate use of petroleum products and stringent exhaust emissions has emphasized the need for alternative green fuels. Although several studies have been conducted to ascertain the performance of acetone-gasoline blends in spark-ignition (SI) engines, limited work has been done to determine the influence of fuel on lubricant oil deterioration. The current study fills the gap through lubricant oil testing by running the engine for 120 h on pure gasoline (G) and gasoline with 10% by volume acetone (A10). Compared to gasoline, A10 produced better results in 11.74 and 12.05% higher brake power (BP) and brake thermal efficiency (BTE), respectively, at a 6.72% lower brake-specific fuel consumption (BSFC). The blended fuel A10 produced 56.54, 33.67, and 50% lower CO, CO₂, and HC emissions. However, gasoline remained competitive due to lower oil deterioration than A10. The flash-point and kinematic viscosity, compared to fresh oil, decreased by 19.63 and 27.43% for G and 15.73 and 20.57% for A10, respectively. Similarly, G and A10 showed a decrease in total base number (TBN) by 17.98 and 31.46%, respectively. However, A10 is more detrimental to lubricating oil due to a 12, 5, 15, and 30% increase in metallic particles like aluminum, chromium, copper, and iron, respectively, compared to fresh oil. Performance additives like calcium and phosphorous in lubricant oil for A10 decreased by 10.04 and 4.04% in comparison to gasoline, respectively. The concentration of zinc was found to be 18.78% higher in A10 when compared with gasoline. A higher proportion of water molecules and metal particles were found in lubricant oil for A10.

A comparative study based on performance and techno-economic analysis of different strategies for PV-Electrolyzer (green) hydrogen fueling incinerator system

The integration of hydrogen in the primary energy mix requires a major technological shift in virtually every energy-related application. This study has attempted to investigate the techno-economic solar photovoltaic (PV) integrated water electrolysis and waste incineration system. Three different strategies, i.e., (i) PV + Battery(Hybrid mode with required batteries); (ii) auto-ignition (Direct coupling); and (iii) PV + Secondary-Electrolyzer(Direct coupling assisted with secondary electrolyzer), have been envisioned. The 'PV + Battery' consume 42.42 % and 15.07 % less energy than the auto-ignition and 'PV + Secondary-Electrolyzer' methods. However, the capital cost of 'PV + Battery' has been calculated to be 15.4 % and 11.8 % more than auto-ignition and 'PV + Secondary-Electrolyzer, respectively.The energy consumption relative to waste input, the 'PV + Battery' method used 80 % less energy, while auto-ignition and 'PV + Secondary-Electrolyzer' showed 70.5 % and 77.5 % less energy, respectively. Furthermore, these approaches showed a vast difference in cost-benefit for the longer run. 'PV + Battery' was forecasted to be 73.3 % and 23.3 % more expensive than auto-ignition and 'PV + Secondary-Electrolyzer' methods, respectively, for 30 years. Overall, this study can benefit from using either of these methods depending on the application, usage scale, and climatic conditions.

Exhaust Emissions Measurement of a Vehicle with Retrofitted LPG System

The aim of this study was to compare and evaluate the production of exhaust emissions from a vehicle with a petrol engine with the Euro 4 emission standard and powered by petrol and LPG (liquefied petroleum gas). The paper presents new possibilities for monitoring exhaust emissions using an exhaust gas analyzer. At the same time, it points out the topicality and significance of the issue in the monitored area. It examines the impact of a change in fuel on emissions. This change is monitored in various areas of vehicle operation. Measurements were performed during real operation, which means that the results are fully usable and applicable in practice. The driving simulation as well as the test conditions correspond to the RDE (Real Driving Emissions) test standard. A commercially available car was first selected to perform the tests, which was first measured in the original configuration (petrol drive). Based on real-time RDE driving tests, it is possible to determine the number of exhaust emissions. Subsequently, the same measurements were performed with the same vehicle, but the vehicle’s propulsion was changed to LPG. The vehicle was equipped with an additional system that allowed the vehicle to be powered by LPG. The results from the individual driving tests allowed the determination of the exhaust emissions. Emissions of CO (carbon monoxide), CO2 (carbon dioxide), HC (hydrocarbons), and NOx (nitrogen oxides) were monitored as a matter of priority. Through the driving tests, it was found that the gasoline combustion produced higher CO (1.926 g/km) and CO2 (217.693 g/km) emissions compared to the combustion of liquefied gas, where the concentration of the CO emissions was 1.892 g/km and that of the CO2 emissions was 213.966 g/km. In contrast, the HC (0.00397 g/km) and NOx (0.03107 g/km) emissions were lower when petrol was burned. During LPG combustion, the HC emissions reached 0.00430 g/km, and the NOx emissions reached 0.05134 g/km. At the end of the research, the authors compared the emissions determined by real driving (in g/km) with the emission values produced by the emission standard EURO 4 and the certificate of conformity (COC). Practical measurements showed that the vehicle produced excessive amounts of CO when burning gasoline. This production is 0.926 g/km higher and 0.892 g/km higher when burning LPG compared to the limit set by the Euro 4 Emission Standard. The difference is even greater than the limit value stated in the COC document. For other substances, the monitored values are in the norm and are even far below the permitted value

Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

The prevailing massive exploitation of conventional fuels has staked the energy accessibility to future generations. The gloomy peril of inflated demand and depleting fuel reservoirs in the energy sector has supposedly instigated the urgent need for reliable alternative fuels. These very issues have been addressed by introducing oxyhydrogen gas (HHO) in compression ignition (CI) engines in various flow rates with diesel for assessing brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The enrichment of neat diesel fuel with 10 dm3/min of HHO resulted in the most substantial decrease in BSFC and improved BTE at all test speeds in the range of 1000–2200 rpm. Moreover, an Artificial Intelligence (AI) approach was employed for designing an ANN performance-predicting model with an engine operating on HHO. The correlation coefficients (R) of BSFC and BTE given by the ANN predicting model were 0.99764 and 0.99902, respectively. The mean root errors (MRE) of both parameters (BSFC and BTE) were within the range of 1–3% while the root mean square errors (RMSE) were 0.0122 kg/kWh and 0.2768% for BSFC and BTE, respectively. In addition, ANN was coupled with the response surface methodology (RSM) technique for comprehending the individual impact of design parameters and their statistical interactions governing the output parameters. The R2 values of RSM responses (BSFC and BTE) were near to 1 and MRE values were within the designated range. The comparative evaluation of ANN and RSM predicting models revealed that MRE and RMSE of RSM models are also well within the desired range but to be outrightly accurate and precise, the choice of ANN should be potentially endorsed. Thus, the combined use of ANN and RSM could be used effectively for reliable predictions and effective study of statistical interactions.

Response Surface Methodology Routed Optimization of Performance of Hydroxy Gas Enriched Diesel Fuel in Compression Ignition Engines

In this study, the response surface methodology (RSM) optimization technique was employed for investigating the impact of hydroxy gas (HHO) enriched diesel on performance, acoustics, smoke and exhaust gas emissions of the compression ignition (CI) engine. The engine was operated within the HHO flow rate range of 0–10 L/min and engine loads of 15%, 30%, 45%, 60% and 75%. The results disclosed that HHO concentration and engine load had a substantial influence on the response variables. Analysis of variance (ANOVA) results of developed quadratic models indicated the appropriate fit for all models. Moreover, the optimization of the user-defined historical design of an experiment identified an optimum HHO flow rate of 8 L/min and 41% engine load, with composite desirability of 0.733. The responses corresponding to optimal study factors were 25.44%, 0.315 kg/kWh, 117.73 ppm, 140.87 ppm, 99.37 dB, and 1.97% for brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), CO, HC, noise, and smoke, respectively. The absolute percentage errors (APEs) of RSM were predicted and experimental results were below 5%, which vouched for the reliable use of RSM for the prediction and optimization of acoustics and smoke and exhaust emission characteristics along with the performance of a CI engine.

Refining and Reuse of Waste Lube Oil in SI Engines: A Novel Approach for a Sustainable Environment

The protection of the environment and pollution control are issues of paramount importance. Researchers today are engrossed in mitigating the harmful impacts of petroleum waste on the environment. Lubricating oils, which are essential for the smooth operation of engines, are often disposed of improperly after completing their life. In the experimental work presented in this paper, deteriorated engine oil was regenerated using the acid treatment method and was reused in the engine. The comparison of the properties of reused oil, the engine’s performance, and the emissions from the engine are presented. The reuse of regenerated oil, the evaluation of performance, and emissions establish the usefulness of the regeneration of waste lubricating oil. For the used oil, total acid number (TAN), specific gravity, flash point, ash content, and kinematic viscosity changed by 60.7%, 6.7%, 4.4%, 96%, and 15.5%, respectively, compared with fresh oil. The regeneration partially restored all the lost lubricating oil properties. The performance parameters, brake power (BP), brake specific fuel consumption (BSFC), and exhaust gas temperature (EGT) improved with regenerated oil in use compared with used oil. The emissions CO and NOX contents for acid-treated oil were 9.7% and 17.3% less in comparison with used oil, respectively. Thus, regenerated oil showed improved performance and oil properties along with significantly reduced emissions when employed in an SI engine.

Use of diesel and emulsified diesel in CI engine: A comparative analysis of engine characteristics

Despite a number of efforts to evaluate the utility of water-diesel emulsions (WED) in CI engine to improve its performance and reduce its emissions in search of alternative fuels to combat the higher prices and depleting resources of fossil fuels, no consistent results are available. Additionally, the noise emissions in the case of WED are not thoroughly discussed which motivated this research to analyze the performance and emission characteristics of WED. Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) were calculated at 1600 rpm within 15%-75% of the load range. Similarly, the contents of NOx, CO, and HC, and level of noise and smoke were measured varying the percentage of water from 2% to 10% gradually for all values of loads. BTE in the case of water emulsified diesel was decreased gradually as the percentage of water increased accompanied by a gradual increase in BSFC. Thus, WED10 showed a maximum 13.08% lower value of BTE while BSFC was increased by 32.28%. However, NOx emissions (21.8%) and smoke (48%) were also reduced significantly in the case of WED10 along with an increase in the emissions of HC and CO and noise. The comparative analysis showed that the emulsified diesel can significantly reduce the emission of NOx and smoke, but it has a negative impact on the performance characteristics and HC, CO, and noise emissions which can be mitigated by trying more fuels variations such as biodiesel and using different water injection methods to decrease dependency on fossil fuels and improve the environmental impacts of CI engines.

Application of ANN to predict performance and emissions of SI engine using gasoline-methanol blends

The deployment of methanol like alternative fuels in engines is a necessity of the present time to comprehend power requirements and environmental pollution. Furthermore, a comprehensive prediction of the impact of the methanol-gasoline blend on engine characteristics is also required in the era of artificial intelligence. The current study analyzes and compares the experimental and Artificial Neural Network (ANN) aided performance and emissions of four-stroke, single-cylinder SI engine using methanol-gasoline blends of 0%, 3%, 6%, 9%, 12%, 15%, and 18%. The experiments were performed at engine speeds of 1300-3700 rpm with constant loads of 20 and 40 psi for seven different fractions of fuels. Further, an ANN model has developed setting fuel blends, speed and load as inputs, and exhaust emissions and performance parameters as the target. The dataset was randomly divided into three groups of training (70%), validation (15%), and testing (15%) using MATLAB. The feedforward algorithm was used with tangent sigmoid transfer active function (tansig) and gradient descent with an adaptive learning method. It was observed that the continuous addition of methanol up to 12% (M12) increased the performance of the engine. However, a reduction in emissions was observed except for NOx emissions. The regression correlation coefficient (R) and the mean relative error (MRE) were in the range of 0.99100-0.99832 and 1.2%-2.4% respectively, while the values of root mean square error were extremely small. The findings depicted that M12 performed better than other fractions. ANN approach was found suitable for accurately predicting the performance and exhaust emissions of small-scaled SI engines.

Maximising Yield and Engine Efficiency Using Optimised Waste Cooking Oil Biodiesel

In this study, waste cooking oil (WCO) was used as a feedstock for biodiesel production, where the pretreatment of WCO was performed using mineral acids to reduce the acid value. The response surface methodology (RSM) was used to create an interaction for different operating parameters that affect biodiesel yield. The optimised biodiesel yield was 93% at a reaction temperature of 57.50 °C, catalyst concentration 0.25 w/w, methanol to oil ratio 8.50:1, reaction stirring speed 600 rpm, and a reaction time of 3 h. Physicochemical properties, including lower heating value, density, viscosity, cloud point, and flash point of biodiesel blends, were determined using American Society for Testing and Materials (ASTM) standards. Biodiesel blends B10, B20, B30, B40, and B50 were tested on a compression ignition engine. Engine performance parameters, including brake torque (BT), brake power (BP), brake thermal efficiency (BTE), and brake specific fuel consumption (BSFC) were determined using biodiesel blends and compared to that of high-speed diesel. The average BT reduction for biodiesel blends compared to HSD at 3000 rpm were found to be 1.45%, 2%, 2.2%, 3.09%, and 3.5% for B10, B20, B30, B40, and B50, respectively. The average increase in BSFC for biodiesel blends compared to HSD at 3500 rpm were found to be 1.61%, 5.73%, 8.8%, 12.76%, and 18% for B10, B20, B30, B40, and B50, respectively.

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

This paper presents a comprehensive step-wise methodology for implementing industry 4.0 in a functional coal power plant. The overall efficiency of a 660 MWe supercritical coal-fired plant using real operational data is considered in the study. Conventional and advanced AI-based techniques are used to present comprehensive data visualization. Monte-Carlo experimentation on artificial neural network (ANN) and least square support vector machine (LSSVM) process models and interval adjoint significance analysis (IASA) are performed to eliminate insignificant control variables. Effective and validated ANN and LSSVM process models are developed and comprehensively compared. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. A detailed investigation of efficient power generation is presented under 50%, 75%, and 100% power plant unit load. Up to 7.20%, 6.85%, and 8.60% savings in heat input values are identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s overall thermal efficiency.

Modeling Viscosity and Density of Ethanol-Diesel-Biodiesel Ternary Blends for Sustainable Environment

Rapid depletion in fossil fuels, inflation in petroleum prices, and rising energy demand have forced towards alternative transport fuels. Among these alternative fuels, diesel-ethanol and diesel-biodiesel blends gain the most attention due to their quality characteristics and environmentally friendly nature. The viscosity and density of these biodiesel blends are slightly higher than diesel, which is a significant barrier to the commercialization of biodiesel. In this study, the density and viscosity of 30 different ternary biodiesel blends was investigated at 15 °С and 40 °С, respectively. Different density and viscosity models were developed and tested on biodiesel blends soured from different feedstock’s including palm, coconut, soybean, mustard, and calophyllum oils. The prognostic ability and precisions of these developed models was assessed statistically using Absolute Percentage Error (APE) and Mean Absolute Percentage Error (MAPE). The MAPE of 0.045% and 0.085% for density model and 1.85%, 1.41%, 3.48% and 2.27%, 1.85%, 3.50% for viscosity models were obtained on % volume and % mass basis. These developed correlations are useful for ternary biodiesel blends where alcohols are the part of biodiesel blends. The modeled values of densities and viscosities of ternary blends were significantly comparable with the measured densities and viscosities, which are feasible to avoid the harm of vehicles’ operability.

Combustion Kinetics Characteristics of Solid Fuel in the Sintering Process

In order to systematically elucidate the combustion performance of fuel during sintering, this paper explores the influence of three factors, namely coal substitution for coke, quasi-particle structure and the coupling effect with reduction and oxidation of iron oxide, on fuel combustion characteristics, and carries out the kinetic calculation of monomer blended fuel (MBF) and quasi-granular fuel (QPF). The results show that replacing coke powder with anthracite can accelerate the whole combustion process. MBF and QPF are more consistent with the combustion law of the double-parallel random pore model. Although the quasi-particle structure increases the apparent activation energy of fuel combustion, it can also produce a heat storage effect on fuel particles, improve their combustion performance, and reduce the adverse effect of diffusion on the reaction process. In the early stage of reaction, the coupling between combustion of volatiles and reduction of iron oxide is obvious. The oxidation of iron oxide will occur again when the combustion reaction of fuel is weakened.

Thermodynamic Performance Analysis of Hydrofluoroolefins (HFO) Refrigerants in Commercial Air-Conditioning Systems for Sustainable Environment

Global warming is one of most severe environmental concerns that our planet is facing today. One of its causes is the previous generation of refrigerants that, upon release, remain in the atmosphere for longer periods and contribute towards global warming. This issue could potentially be solved by replacing the previous generation’s high global warming potential (GWP) refrigerants with environmentally friendly refrigerants. This scenario requires an analysis of new refrigerants for a comparison of the thermodynamic properties of the previously used refrigerants. In the present research, a numerical study was conducted to analyze the thermodynamic performance of specifically low GWP hydrofluoroolefens (HFO) refrigerants for an actual vapor compression refrigeration cycle (VCRC) with a constant degree of 3 K superheat. The output parameters included the refrigeration effect, compressor work input, the coefficient of performance (COP), and the volumetric refrigeration capacity (VRC), all of which were calculated by varying the condenser pressure from 6 to 12 bars and vapor pressure from 0.7 to 1.9 bars. Results showed that R1234ze(Z) clearly possessed the desired thermodynamic performance. The drop in refrigeration effect for R1234ze(Z) was merely 14.6% less than that of R134a at a 12 bar condenser pressure; this was minimum drop among candidate refrigerants. The drop in the COP was the minimum for R1234ze(Z)—5.1% less than that of R134a at a 9 bar condenser pressure and 4.7% less than that of R134a at a 1.9 bar evaporator pressure, whereas the COP values of the other refrigerants dropped more drastically at higher condenser pressures. R1234ze(Z) possessed favorable thermodynamic characteristics, with a GWP of 7, and it can serve as an alternative refrigerant for refrigeration systems for a sustainable environment.