Multicriteria Decision-Making Technique for Choosing the Optimal Ammonia Energy Share in an Ammonia-Biodiesel-Fueled Reactivity-Controlled Compression Ignition Engine.

Low-temperature combustion paired with the use of carbon-free ammonia and carbon-neutral biofuels is a novel approach for improving performance, reducing greenhouse gases, and reducing regulated emissions. Reactivity-controlled compression ignition (RCCI), a low-temperature combustion technology, dramatically reduces NOx and smoke emissions compared to traditional engines. Ammonia can be projected as a good transit fuel in the journey toward achieving net zero emissions and cleaner energy. This study examines the impact of ammonia energy premixing fraction (AEPF) (20, 30, 40, and 50%) as a low-reactive fuel (LRF) and algal biodiesel as a high-reactive fuel on the performance and emission characteristics of a single-cylinder, water-cooled 3.5 kW CI engine at a constant speed of 1500 rpm under various loading conditions. The research results indicate that the 40% ammonia share RCCI mode exhibited a reduction in carbon dioxide (CO2) by 14.16%, nitrogen oxide (NOx) by 22.6%, and smoke by 42.1%, with an 11.5% improvement in thermal efficiency compared to the neat biodiesel-fueled conventional engine. Furthermore, the analytical hierarchy process (AHP) will be used in conjunction with the technique for order of preference by similarity to ideal solution (TOPSIS) of multiple criteria decision-making techniques to determine the optimal energy share in the RCCI combustion with the goal of achieving superior thermal efficiency and lower emissions. According to the AHP-TOPSIS study findings, AEPF40 is the best choice for all engine loads.

Investigation on Ammonia-Biodiesel Fueled RCCI Combustion Engine Using a Split Injection Strategy.

Advanced combustion concepts in compression ignition are emerging as one of the most promising solutions to reduce nitrogen oxides (NOx) and particle emissions without sacrificing fuel efficiency. Among many advanced combustion concepts, reactive controlled compression ignition (RCCI) can achieve a wider working range. In this study, to implement RCCI operation, ammonia gas is introduced through the manifold as a low-reactive fuel, and biodiesel is injected directly as a high-reactivity fuel with a 40:60 energy ratio. The effect of biodiesel split ratio in a split injection strategy (pre- and main injections) is examined under varied load conditions, and the results are compared with ammonia/biodiesel single injection. Results indicate that the use of the 45% biodiesel split ratio at full load boosts the peak in-cylinder pressure and heat release rate and shifts the peak occurrence toward the top dead center (TDC). An increase in brake thermal efficiency (BTE) to 36.22% and reduced brake specific energy consumption (BSEC) to 8.75 MJ/kWh are 12.33% higher and 19.31% lower than ammonia/biodiesel single injection. Emissions of HC, CO, and smoke opacity were reduced to 50 ppm, 0.098% vol, and 15.6%, which are 34.21, 39.13, and 33.89% lower, while the emission of NOx was increased to 615 ppm, which is 36.06% higher than the single-injection ammonia/biodiesel RCCI combustion.

Experimental Investigation on the Effect of Graphene Oxide in Higher Alcohol Blends and Optimization of Injection Timing Using an ANN Method.

The use of alternative fuels in diesel engines has become more widespread due to a number of factors, including dwindling petroleum supplies, increasing prices for conventional fossil fuels, and environmental worries about pollutants and greenhouse gas emissions from internal combustion engines. Efficiency and emissions need to be appropriately balanced. Alcohols act as oxygenated fuels similar to octanol, offering a number of benefits over traditional fuels and can boost efficiency, enhance combustion, and reduce air pollution. Therefore, the research aimed to enhance the performance and combustion characteristics of a diesel and octanol blend using graphene oxide (GO) nanoparticles as a fuel additive in a single-cylinder diesel engine while reducing emissions. Research findings will contribute significantly to improving the physical and chemical properties of diesel and octanol blends, thereby mitigating the challenges of limited petroleum reserves and environmental concerns. A range of different blends of diesel and octanol were prepared on a volume/volume basis in proportions of D70OCT30, D60OCT40, and D50OCT50, and then GO was added as a fuel additive to the abovementioned blends in varied proportions (40, 60, and 80 ppm) resulting in nine blends. These blends were analyzed in terms of various performance, combustion, and emission characteristics, and the obtained results helped to shed light on the impact of GO as a fuel additive. The results indicated that the fuel blend D70OCT30GO0.006 yielded the highest values. Furthermore, it is highly imperative that we develop a model that can be used to predict engine behavior and its stability without having to run an engine. For this, a data-driven artificial neural network (ANN) model was developed to predict the optimized injection timing for better combustion and reduced emission. The efficiency and prediction capabilities of the model were compared to the experimental data, which indicated that the ANN model had a better prediction score. The injection timing of the engine was optimized from 21 °CA to 21.5 °CA, which increased the efficiency by 1%. The research findings showed significantly improved physical and chemical properties of the blends, thereby mitigating the challenges of limited petroleum reserves and environmental concerns.

Experimental Study on Sustainability Involving the Pugh Matrix on Emission Values of High-Temperature Air in the Premixed Charged Compression Ignition Engine.

The main aim of the study was to reduce carbon emissions in the atmosphere using a novel Andropogon narudus (AN) biofuel using higher air temperatures and reducing the consumption of conventional fossil fuel (diesel). The use of a heat exchange chamber within the air intake manifold is a popular method to reduce hydrocarbon (HC) and carbon monoxide (CO) emissions during cold starts. A premixed charged compression ignition engine in the dual-fuel mode was used in this study with raw diesel, raw AN oil, AN70+D30, AN80+D20, AN80+D20 (35 °C), AN80+D20 (40 °C), and AN80+D20 (45 °C). A chamber was designed and analyzed to measure the exit temperature and density change and to determine the reduction in volumetric efficiency of the engine, using Ansys Fluent software. A sustainability assessment study was performed to understand the feasibility of the fuel and the design using the Pugh Matrix. The fuel AN80+D20 with an air temperature of 45 °C was found to be superior to all other fuels in terms of brake thermal efficiency, reaching at 32.1%. D100 used the least amount of energy, whereas AN80+D20 used the most. Engine HC emission was at the lowest (45.01 ppm) for AN80+D20 fuel at 45 °C air input and reached the highest (50 ppm) for AN100 fuel. With an air temperature of 45 °C, CO emission was at its lowest for AN80+D20 gasoline (0.018%) and was at its highest for AN100 (0.072%). Nitrogen oxide emissions were the highest for AN80+D20 fuel with an air temperature of 45 °C, with an air concentration of 1254 ppm, whereas they were the lowest for AN100 (900 ppm). CO2 values were reduced, with D100 showing the lowest levels and AN100 showing the highest. The smoke emission was minimum for AN80+D20 fuel at 45 °C, with a smoke number of 15 compared to 33 for D100 fuel. As per the Pugh Matrix assessment, AN80+D20 with 35 °C air temperature had higher scores compared to all of the other fuel mixtures.

Solar Salt with Carbon Nanotubes as a Potential Phase Change Material for High-Temperature Applications: Investigations on Thermal Properties and Chemical Stability.

Nano-enhanced phase change materials are highly employed for an enhanced heat-transfer process. The current work reports that the thermal properties of solar salt-based phase change materials were enhanced with carbon nanotubes (CNTs). Solar salt (60:40 of NaNO3/KNO3) with a phase change temperature and enthalpy of 225.13 °C and 244.76 kJ/kg, respectively, is proposed as a high-temperature PCM, and CNT is added to improve its thermal conductivity. The ball-milling method was employed to mix CNTs with solar salt at various concentrations of 0.1, 0.3, and 0.5% by weight. SEM images display the even distribution of CNTs with solar salt, with the absence of cluster formations. The thermal conductivity, phase change properties, and thermal and chemical stabilities of the composites were studied before and after 300 thermal cycles. FTIR studies indicated only physical interaction between PCM and CNTs. The thermal conductivity was enhanced with an increase in CNT concentration. The thermal conductivity was enhanced by 127.19 and 125.09% before and after cycling, respectively, in the presence of 0.5% CNT. The phase change temperature decreased by around 1.64% after adding 0.5% CNT, with a decrease of 14.67% in the latent heat during melting. TGA thermograms indicated the weight loss was initiated at about 590 and 575 °C before and after thermal cycling, after which it was rapid with an increase in temperature. Thermal characterization of CNT-enhanced solar salt indicated that the composites could be used as phase change materials for enhanced heat-transfer applications.

Exploring Antibacterial Properties of Bioactive Compounds Isolated from Streptomyces sp. in Bamboo Rhizosphere Soil.

The increasing concern over multidrug resistance in pathogens has led to an ongoing search for novel antibiotics derived from soil actinobacteria. In this current investigation, actinobacteria were isolated from the rhizosphere of bamboo plants collected within the Megamalai forest of the Western Ghats in the Theni zone of Tamil Nadu, India. These actinobacteria were subjected to characterization, and their growth conditions were optimized to enhance the production of bioactive compounds. To assess antibacterial properties, the isolated Actinobacteria underwent testing using the agar plug method. The strain exhibiting notable antibacterial activity underwent further characterization through 16s rRNA gene sequencing and subsequent phylogenetic analysis. Employing response surface methodology (RSM), cultural conditions were fine-tuned. Bioactive compounds were extracted from the culture medium using ethyl acetate, and their antibacterial and antioxidant effects were evaluated through disc diffusion and DPPH radical scavenging methods, respectively. Ethyl acetate extracts were analyzed by using FT-IR and GC-MS techniques. In total, nine strains of Actinobacteria were isolated from the rhizosphere soil of bamboo. Among these, strain BS-16 displayed remarkable antibacterial activity against three strains: Staphylococcus aureus (19 mm), Bacillus subtilis (12 mm), and Streptococcus pyogenes (10 mm). This strain was identified as Streptomyces sp. The optimal conditions for bioactive compound production were determined as follows: malt extract (10 g), yeast extract (5 g), dextrose (5 g), pH 6.5, and temperature 30 °C. After a 7-day incubation period, the results showed a 6% increase in production. The ethyl acetate fraction derived from strain BS-16 exhibited dose-dependent antibacterial and antioxidant activities. FT-IR and GC-MS analyses revealed the presence of active compounds with antibacterial effects within the extract. Consequently, further investigation into the BS-16 strain holds promise for scaling up the production of bioactive compounds possessing antibacterial and antioxidant properties.

Investigation into the Ideal Concoction for Performance and Emissions Enhancement of Jatropha Biodiesel-Diesel with CuO Nanoparticles Using Response Surface Methodology.

The present work covers the preparation of biodiesel from jatropha oil through the transesterification process followed by its characterization, and furthermore, performance and emission analyses were done in terms of blending biodiesel with fossil diesel and CuO nanoparticles. Jatropha biodiesel blends (B10, B20, and B30) were chosen for this preliminary investigation based on the observation that B20 outperformed other blends. Next stage B20 with copper oxide (CuO) nanoparticle concentrations of 25, 50, 75, and 50 ppm are used to examine the performance and emission characteristics of a constant speed single cylinder, 4-stroke, 3.5 kW compression ignition (CI) engine. Finally, The response surface methodology (RSM) was utilized to determine the optimal nanoparticle concentration for B20. The results revealed that the blend of B20 with 80 ppm nanoparticles had the highest desirability (0.9732), and the developed RSM model was able to predict engine responses with a mean absolute percentage error (MAPE) of 3.113%. A confirmation test with an error in prediction of less than 5% verified the model's adequacy. When comparing optimized B20CuO80 to diesel, brake specific energy consumption (BSEC) increased by 8.49% and brake thermal efficiency (BTE) was lowered by 3.34%. Hydrocarbon (HC), carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxide (NOx), and smoke emissions were reduced by 3.66% and 2.88%, 4.78%, 22.9%, and 20.54%, respectively, at 80% load. As a result, the B20 blend with nanoparticle concentrations of 80 ppm may be used in current diesel engines without engine modification.

Challenges and Opportunities of Low Viscous Biofuel-A Prospective Review.

Under the roof of solid industrialization and accelerated intensification of multiple ranges of mobilization, a huge rise in precious fuel consumption and pollution was observed. Based on the recent hardships of fossil fuels, experts are undoubtedly eager in carrying out their research in renewable environment-friendly fuels. There have been many reviews of works considering the parameters and standards of biodiesel, which is only from various vegetable and seed oils. But very little review work was carried out on only plant-based biofuel. Plant-based fuel has a lower viscosity and higher volatility properties. The target of this review was to make a bridge to overcome these research gaps. This review extensively studies the biological background, production outcome, properties, and reliability of plant-based biofuel and also deeply investigates the feasibility of usage in a diesel engine. From deep investigation it was identified that most of the low viscous fuel had higher brake thermal efficiency (BTE) (2% to 4%) and NOx emission (5% to 10%) than high viscous biodiesel. The formation of hydrocarbon (HC), CO, and smoke emission was similar to high viscous biodiesel. Overall, the low viscous fuel effectively improves the engine behaviors.

Performance and Emission Analysis of Biodiesel Blends in a Low Heat Rejection Engine with an Antioxidant Additive: An Experimental Study.

The rapid depletion of crude oil and environmental degradation necessitate the search for alternative fuel sources for internal combustion engines. Biodiesel is a promising alternative fuel for compression ignition (CI) engines due to its heat content and combustion properties. Biodiesel blends are used in various vehicles and equipment, such as cars, trucks, buses, off-road vehicles, and oil furnaces. Biodiesel can reduce emissions from CI engines by up to 75% and improve engine durability due to its high lubricity. However, biodiesel has some drawbacks, including a performance reduction and increased nitrogen oxide emissions. Therefore, this study aims to investigate using environmentally available biodiesel in a low-heat rejection engine and an antioxidant additive to enhance the performance and reduce nitrogen oxide emissions. India currently has several biodiesel sources, including mango seed oil, mahua oil, and pongamia oil, which can be effectively utilized in CI engines by adding l-ascorbic acid. The experimental work involves a single-cylinder 4-stroke water-cooled direct injection CI engine with a power output of 5.2 kW. The engine's cylinder head, piston head, and valves are coated with lanthanum oxide using the plasma spray coating technique, with a thickness of 0.5 mm. The coated and uncoated engines are tested with different proportions of mahua oil, mango seed oil, and pongamia oil. The results show that the engine's performance is significantly improved compared to the baseline engine at all loads. Additionally, these biodiesels exhibit a notable reduction in nitrogen oxide emissions when combined with l-ascorbic acid.

Glass Fiber-Epoxy Composites with Carbon Nanotube Fillers for Enhancing Properties in Structure Modeling and Analysis Using Artificial Intelligence Technique.

Hybrid composite materials are a form of material that incorporates more than one type of reinforcement into a matrix to attain enhanced qualities. This usually includes the use of nanoparticle fillers in classic advanced composites with fiber reinforcements such as carbon or glass. In the current investigation, the impact of carbon nanopowder filler on the wear and thermal performance of the chopped strand mat E-glass fiber-reinforced epoxy composite (GFREC) were analyzed. Multiwall carbon nanotube (MWCNT) fillers were used; they react with the resin system to contribute a significant improvement of properties in the polymer cross-linking web. The experiments were carried out employing the central composite method of design of experiment (DOE). A polynomial mathematical model was created using response surface methodology (RSM). To forecast the wear rate of composites, four machine learning (ML) regression models were built. The study's findings indicate that the addition of carbon nanopowder has a substantial impact on the wear behavior of composites. This is mostly owing to the homogeneity created by the carbon nanofillers in uniformly dispersing the reinforcements in the matrix phase. Results revealed that a load of 1.005 kg, a sliding velocity of 1.499 m/s, a sliding distance of 150 m, and 15 wt % of filler were found to be the optimal parameters for the efficient reduction of specific wear rate. Composites with 10 and 20% carbon contents exhibit lower thermal expansion coefficients than plain composites. These composites' coefficients of thermal expansion fell by 45 and 9%, respectively. If the carbon proportion increases beyond 20%, so will the thermal coefficient of expansion.

Effect of Kariba Weed Biodiesel Blended with n-Pentane on the Chosen Parameters of a Ceramic-Coated Thermal Barrier Direct Injection Diesel Engine.

In this experimental investigation, Kariba weed biodiesel (KSB) blended with n-pentane has been tested in conventional and ceramic-coated thermal barrier engines, and the results have been compiled and presented. A single-cylinder, four-stroke, direct injection diesel engine has been used as the test engine with eddy current dynamometer loading as used in the experimental setup. The tests were repeated in various ambient conditions to get an optimal value. Ceramic coating has been done with partially stabilized zirconia by the plasma arc spraying process. Among the quantum of tests conducted, 90% KSB blended with 10% n-pentane showed appreciable results when it was compared with the test fuel (neat diesel). The brake thermal efficiency and brake-specific fuel consumption were found to be better when compared with neat diesel. At increasing load, unburnt hydrocarbon, carbon monoxide, and smoke opacity emissions were appreciably reduced.

Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder.

The purpose of the research is to predict the compressive and flexural strengths of the concrete mix by using waste marble powder as a partial replacement of cement and sand, based on the experimental data that was acquired from the laboratory tests. In order to accomplish the goal, the models of Support vector machines, Support vector machines with bagging and Stochastic, Linear regression, and Gaussian processes were applied to the experimental data for predicting the compressive and flexural strength of concrete. The effectiveness of models was also evaluated by using statistical criteria. Therefore, it can be inferred that the gaussian process and support vector machine methods can be used to predict the respective outputs, i.e., flexural and compressive strength. The Gaussian process and Support vector machines Stochastic predicts better outcomes for flexural and compressive strength because it has a higher coefficient of correlation (0.8235 and 0.9462), lower mean absolute and root mean squared error values as (2.2808 and 1.8104) and (2.8527 and 2.3430), respectively. Results suggest that all applied techniques are reliable for predicting the compressive and flexural strength of concrete and are able to reduce the experimental work time. In comparison to input factors for this data set, the number of curing days followed by the CA, C, FA, w, and MP is essential in predicting the flexural and compressive strength of a concrete mix for this data set.