Evaluation of a compression ignition engine performance and emission characteristics using diesel-essential oil blends of high orange oil content

In this research, waste stream essential oil such as orange oil is used as a diesel fuel partial replacement to be tested in a diesel engine. Like diesel fuel, orange oil does not contain any oxygen since it is constituted of limonene (a colourless liquid aliphatic hydrocarbon) and has almost similar density. A 6-cylinder diesel engine is operated using various blends of orange and diesel fuel. The engine was operated with three different fuel blends: neat diesel, 74% diesel + 26% orange oil (D74O26) and 59% diesel + 41% orange oil (D59O41). All the orange oil blends produced nearly the same brake power from the engine experiment compared to neat diesel fuel. Furthermore, all orange oil blends emit less particulate matter, and the ‘count mean diameter’ of the emitted particles is also lower than base diesel. Based on the obtained results, these blends can be suggested to be used in a diesel engine

Environmental Analysis, Monitoring, and Process Control Strategy for Reduction of Greenhouse Gaseous Emissions in Thermochemical Reactions

This review paper illustrates the recommended monitoring technologies for the detection of various greenhouse gaseous emissions for solid waste thermochemical reactions, including incineration, pyrolysis, and gasification. The illustrated gas analyzers are based on the absorption principle, which continuously measures the physicochemical properties of gaseous mixtures, including oxygen, carbon dioxide, carbon monoxide, hydrogen, and methane, during thermochemical reactions. This paper illustrates the recommended gas analyzers and process control tools for different thermochemical reactions and aims to recommend equipment to increase the sensitivity, linearity, and dynamics of various thermochemical reactions. The equipment achieves new levels of on-location, real-time analytical capability using FTIR analysis. The environmental assessment study includes inventory analysis, impact analysis, and sensitivity analysis to compare the mentioned solid waste chemical recycling methods in terms of greenhouse gaseous emissions, thermal efficiency, electrical efficiency, and sensitivity analysis. The environmental impact assessment compares each technology in terms of greenhouse gaseous emissions, including CO2, NOx, NH3, N2O, CO, CH4, heat, and electricity generation. The conducted environmental assessment compares the mentioned technologies through 15 different emission-related impact categories, including climate change impact, ecosystem quality, and resource depletion. The continuously monitored process streams assure the online monitoring of gaseous products of thermochemical processes that enhance the quality of the end products and reduce undesired products, such as tar and char. This state-of-the-art monitoring and process control framework provides recommended analytical equipment and monitoring tools for different thermochemical reactions to optimize process parameters and reduce greenhouse gaseous emissions and undesired products

Energy for sustainable future

Energy and the environment are interrelated, and they are critical factors that influence the development of societies [...]</jats:p

Exploring the Potential of Lignocellulosic Biomass-Derived Polyoxymethylene Dimethyl Ether as a Sustainable Fuel for Internal Combustion Engines

The most effective way to reduce internal combustion engine emissions is to use a sustainable alternative fuel that contains oxygen molecules. Alternative fuels may be used to address a future global energy crisis. Different oxygenated alternative fuels have been investigated in internal combustion engines. Polyoxymethylene di-methylene ether (PODE), which contains 3–5 CH2O groups, is currently superior in the field of oxygenated fuels due to its physical and chemical properties. Furthermore, using PODE as a fuel does not necessitate any significant engine modifications. When compared to standard diesel fuel, the use of PODE results in near stoichiometric combustion with less hazardous exhaust gas. It also significantly reduces NOx emissions due to the lack of C-to-C bonds. Several articles in the literature were found on the manufacturing and application processes for the production of PODE. However, the current review focuses primarily on simplifying the various production technologies, the physical and chemical properties of PODEn and its advantages and disadvantages in ICEs, PODEn application in internal combustion engines and its characteristics, PODE spray analysis, and measurements of the fuel’s physical and chemical characteristics. This review emphasizes the fact that PODE can be used as a sole fuel or in conjunction with fossil fuels and advanced combustion technologies. Because C-C bonds and higher oxygen molecules are not available, the trade-off relationship between nitrogen oxides and soot production is avoided when PODEn is used as a fuel, and combustion efficiency is significantly improved

Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm.

In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA's performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA's performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis

Techno-Economic Analysis of Hybrid Diesel Generators and Renewable Energy for a Remote Island in the Indian Ocean Using HOMER Pro

This study is about the electrification of the remote islands in the Indian Ocean that were severely affected by the tsunami in the 2004 earth earthquake. To supply electricity to the islands, two diesel generators with capacities of 110 kW and 60 kW were installed in 2019. The feasibility of using renewable energy to supplement or replace the units in these two generators is investigated in this work. In 2019, two diesel generators with capacities of 110 kW and 60 kW were installed in the islands to supply electricity. This work analyses whether the viability of using renewable energy can be used to supplement or replace these two generators. Among the renewable energy options proposed here are a 100 kW wind turbine, solar PV, a converter, and batteries. As a result, the study’s goal is to perform a techno-economic analysis and optimise the proposed hybrid diesel and renewable energy system for a remote island in the Indian Ocean. The Hybrid Optimisation Model for Electric Renewable (HOMER) Pro software was used for all simulations and optimisation for this analysis. The calculation is based on the current diesel price of USD 0.90 per litre (without subsidy). The study found that renewable alone can contribute to 29.2% of renewable energy fractions based on the most optimised systems. The Net Present Cost (NPC) decreased from USD 1.65 million to USD 1.39 million, and the levelised Cost of Energy (CoE) decreased from 0.292 USD/kWh to 0.246 USD/kWh, respectively. The optimised system’s Internal Rate of Return (IRR) is 14% and Return on Investment (ROI) 10%, with a simple payback period of 6.7 years. This study shows that it would be technically feasible to introduce renewable energy on a remote island in Indonesia, where numerous islands have no access to electricity

Optimisation of trimethylolpropane ester synthesis from waste cooking oil methyl ester by response surface methodology, and its physicochemical properties and tribological characteristics

This study is focused on optimising the process variables of trimethylolpropane (TMP) ester synthesis from waste cooking oil methyl ester (WCOME) using response surface methodology with Box–Behnken experimental design in order to maximise the TMP ester (TWCOE biolubricant) yield. The following process variables were optimised: (1) reaction time, (2) TMP-to-WCOME ratio, and (3) sodium methoxide catalyst concentration. The predicted TWCOE biolubricant yield was 97.06 %, which conformed well with the experimental TWCOE biolubricant yield of 96.12 %. The quadratic response surface model demonstrated a robust fit with the experimental data (R² = 0.9888).The physicochemical properties and tribological characteristics of the TWCOE biolubricant were assessed and compared with those of commercial lubricants. The TWCOE biolubricant had a kinematic viscosity of 41.55 mm2/s at 40 °C and 6.93 mm2/s at 100 °C. The TWCOE biolubricant had an acid value of 0.4 mg KOH/g, flash point of 222.2 °C, and viscosity index of 125.30. The coefficient of friction of the TWCOE biolubricant (0.045) was lower than those of the SAE15W40, SAE0W30, and ATF9 lubricants (0.062, 0.088, and 0.089, respectively). However, the average wear scar diameter for the TWCOE lubricant (0.632 mm) was higher than those of commercial lubricants. The favourable lubricating characteristics suggest that the TWCOE biolubricant has the potential for use as an effective lubricant or additive in industrial machinery

Response surface methodology (RSM) for optimizing engine performance and emissions fueled with biofuel: Review of RSM for sustainability energy transition

Response Surface Methodology (RSM) is a statistical method to design experiments and optimize the effect of process variables. RSM is based on the principles of design of experiments or DOE. Design of experiments or DOE is a field of applied statistics that plans, conducts, analyses, and interprets controlled tests to assess factors that affect parameter values. Response surface methodology or RSM uses a statistical method for designing experiments and optimization. Despite the potential of response surface methodology to predict and optimize engine performance and emissions characteristics, a comprehensive review on RSM for biofuels, particularly for internal combustion engines (ICEs), is difficult to find. The review of response surface methodology is sometimes included together with other machine learning approaches such as ANN. Therefore, a review article that is exclusively written to address the specific of RSM for biofuel and ICE is required. This review article offers a fresh perspective on the application of RSM for biofuel in ICE. This article aims to critically review the RSM to optimize engine performance and emissions using biofuel. The study concludes with several possible research gaps for future works of RSM biofuel application. Although response surface methodology or RSM has drawbacks such as extrapolation inaccuracy outside the investigational ranges and discrete variables error, RSM has numerous advantages to design, model, estimate, and optimize biofuel for ICE with satisfactory accuracy. With its prediction and optimization capability, response surface methodology has the potential to assist the development of ICE optimization powered by biofuel for sustainability energy transition

Experimental investigation of a multicylinder unmodified diesel engine performance, emission, and heat loss characteristics using different biodiesel blends: rollout of B10 in Malaysia.

This paper deals with the performance and emission analysis of a multicylinder diesel engine using biodiesel along with an in-depth analysis of the engine heat losses in different subsystems followed by the energy balance of all the energy flows from the engine. Energy balance analysis allows the designer to appraise the internal energy variations of a thermodynamic system as a function of ''energy flows" across the control volume as work or heat and also the enthalpies associated with the energy flows which are passing through these boundaries. Palm and coconut are the two most potential biodiesel feed stocks in this part of the world. The investigation was conducted in a four-cylinder diesel engine fuelled with 10% and 20% blends of palm and coconut biodiesels and compared with B5 at full load condition and in the speed range of 1000 to 4000 RPM. Among the all tested blends, palm blends seemed more promising in terms of engine performance, emission, and heat losses. The influence of heat losses on engine performance and emission has been discussed thoroughly in this paper