Internal combustion engines consistently play a pivotal role in global transportation networks, facilitating economic growth and the interconnection of nations. Since their inception, diesel has been regarded as one of the primary fossil fuels. However, concerns about pollution and energy security drive the demand for alternative fuels. Waste cooking oil biodiesel emerges as a readily available and sustainable resource, capable of being collected and repurposed to reduce the burden on landfills and waste management systems. Using a numerical simulation tool, this study analyzes the performance and emission characteristics of a diesel engine operating on waste cooking oil. The experiments evaluate performance and emissions at various nozzle opening pressure settings, ranging from 180 to 280 bar, and compare the results with diesel. Before the experimental phase, the numerical modeling tool undergoes thorough validation against findings from previous research studies, demonstrating its ability to deliver accurate results. The study finds that blending proportions of waste cooking oil, specifically WCO10 and WCO20, yield results closest to diesel in most cases. This signifies that biodiesels with precise blending percentages can effectively address the growing need for alternative fuels.

Engine oil degradation impacts the operation and performance of an internal combustion engine. As a result, it is crucial to compare the degradation level of engine oils, with a particular emphasis on kinematic viscosity, oxidation, nitration, and critical oil testing characteristics. Suppose engine oil changes too soon without first calculating its remaining usable life. In that case, it wastes already scarce resources and has an unfavourable environmental influence. Engine performance may suffer if oil is changed too late and is of poor quality. In the current research, oil testing is performed on vehicles due to maintenance to find the best moment for changing engine oil. Oil samples were taken randomly from cars that came to an authorized service facility for maintenance, comprising a wide range from the first to the fifth servicing. Oil samples were initially evaluated in a laboratory using a viscometer and Fourier transform-infrared spectroscopy in conformity with industry standards. The samples were then evaluated using an integrated sensor system developed and built by the authors. The determination coefficient R2 = 0.97 showed a significant positive association between major engine oil degradation characteristics such as kinematic viscosity, oxidation, and nitration at reasonable sensitivity. Approximately 8% of the total gathered samples were useable. The research identifies several oil degradation sensor systems that give low-cost, on-site convenient options for oil condition monitoring and predicting the remaining usable life of engine oil.

The quantity of the conventional sources of energy such as petroleum, coal, and the deposits of hydrocarbon are limited in nature. The rapid growth is observed in the industrial and transportation section due to increase in population that has compelled the mankind to look for an alternative to conventional fuels. Biodiesel can be a potential substitute due to its ease of availability, accessibility, lower cost, and nontoxic characteristics. In this study, waste cooking oil (WCO) as the feedstock for biodiesel due to its abundance and low prices is chosen. It has been observed that the high prices of cooking oil have compelled people to reuse it multiple times, which can have negative health consequences such as inflammation, cholesterol issues, diabetes, and cancer. However, if people can obtain WCO at a reasonable cost, they will refrain from reusing it repeatedly. This article presents a numerical method that investigates the effects of variable exhaust valve timing on the performance, emissions, and combustion parameters of WCO and its different blends in a conventional mechanical fuel injection system diesel. The study was conducted using a single-cylinder, water-cooled, in-line diesel engine running at a constant speed. As, it is not possible to determine the values of engine performance, emissions, and combustion parameters for different valve timings experimentally, a numerical method has been adopted. The valve timing range was taken as 46–66° before bottom dead center (BDC) for exhaust valve opening and 6–20° after top dead center (TDC) for exhaust valve closing and the inlet valve opening and closing are constant at 16° b TDC and 33° a BDC, respectively.

Exergy approach is essential for the design and possible operation of the absorption system with energy loss minimization. Operational limitations of absorption refrigeration system are due to Gibbs phase rules-base. Optimized operational decision rules are extracted from machine learning algorithm C4.5 which is suitable for different exergy evaluation methods and reference conditions. This study investigates proposed and investigated classification models for the same which are based on different thermodynamic features and operational design data. ANN model is used to predict the thermodynamic properties of the working fluids using improved thermodynamics properties of data patterns. Mathematical expressions are formulated from validated artificial neural network for predicting specific enthalpy and entropy of water–lithium bromide solution. Exergy design data are estimated on 356 thermodynamic design data pattern of absorption refrigeration system by MATLAB Simulators. Pearson’s correlation heatmap is used for extracting 14 thermodynamic features. 94.38% is the highest performance observed with 12 feature class six classification model.

To explore the effect of hybrid fuels containing microalgae, ethanol, methanol and diesel fuel (base fuel) were blends on combustion, performance and emission characteristics and compare with base fuel. The hybrid fuels were calculated using a single-cylinder, four-stroke, naturally aspirated engine, water-cooled, direct injection, diesel engine was used for the experiments under 20%, 50%, 75%, 100% load and simulated by using a thermodynamic engine simulation tool software. The results show that engine brake torque (EBT) of hybrid fuel (ethanol) emulsions were found to be high and exhaust gas temperature to be low compared with that of base fuel. Hybrid fuel (methanol) emulsions helps to increase the cylinder pressure. With the hybrid fuels blend, the ignition delay period and combustion duration of hybrid fuel blends is increased. With the addition of spirulina microalgae, the ignition delay period of spirulina microalgae-diesel blend fuel is shortened. Engine emission results indicated that spirulina microalgae emulsions fuel reduces the specific particulate matter (PM), soot and smoke emissions except nitrogen oxides (NOX) emissions but carbon dioxide (CO2) emission to be higher compared with base fuel.

The main aim of this work is software simulation for estimating thermodynamic properties (specific enthalpy and specific entropy) of water–lithiumbromide solution using artificial neural network under MATLAB Simulink environment. AI-Simulink simulator is developed by deploying extracted weights and bias from modeled artificial neural networks. Optimized performance is achieved with 2-10-2 ANN architecture which is validated on the basis of mean square error, coefficient of multiple determination (R2), and absolute relative error.

A paper has been published in Renewable Energy, second law-based thermodynamic analysis of Ammonia/Sodium Thiocynate vapour absorption refrigeration (VAR) system. Linghui Zhu and Junjie Gu have done exergy analysis and estimated exergy loss rate using second law analysis under different headings. The present paper attempts to discuss and comments on the calculation procedure, calculated results and mathematical expressions of the past published work of Linghui Zhu and Junjie Gu. Authors have also presented correction against each comment which must be used by future researchers. This study is the appropriate corrigendum for the past published work of Linghui Zhu and Junjie Gu.

The attempt of this work is to comment on the wrongly formulated mathematical expressions by L. Garousi Farshi et al. in their published work which are used for predicting thermodynamic properties of working fluids (Ammonia/LiNO3 and Ammonia/NaSCN). L. Garousi Farshi et al. is also wrongly reported validation part in their published work due to wrong formulation of mathematical expressions of the thermodynamic properties. In addition to that four mathematical expressions are giving results as real number while remaining one is giving as imaginary number. In those above sections of the published work of L. Garousi Farshi et al., substantial changes are required.

In this work, computational intelligence is used to calculate the energy and exergy analysis of lithium bromide?water (LiBr-H2O)-operated vapour absorption refrigeration system. An artificial neural network (ANN) is trained for predicting the thermodynamic properties of the solutions. Mathematical equations are extracted from the trained and validated ANN for predicting the same. Coefficient of multiple determination (R-2) is calculated equal to the unity for test dataset of both thermodynamic properties which justified the use of artificial intelligence. The use of the extracted mathematical expressions can be leveraged with any programming language for estimating specific enthalpy and specific entropy of the working fluid, as described in this work. Results of energy and exergy analysis are compared with the published literature work.

•Full description of proposed fabricated heat exchanger with different corrugated and non-corrugated pies.•Different pictorial configuration of pipes according to different pitch and depth.•ANN modeling for predicting the heat transfer performance of proposed fabricated heat exchanger.•Systematic procedure of formulating the mathematical expressions from validated ANN for predicting the same.

A paper has been published in International Journal of Refrigeration, second law analysis of vapour absorption refrigeration (VAR) system using different mathematical expressions which obey thermodynamics principles. The present paper attempts to discuss and comment on the paper. Addition or subtraction of exergy of the heat flux (or exergy of heat transfer) in mathematical expression of internal irreversibility, are not meeting to direction principles of exergy flow. Therefore, internal irreversibility expressions must be corrected for future research based on second law in the field of VAR system.

In this study, artificial neural network (ANN) has been used as a new approach for carrying out the energy analysis of a single-stage absorption refrigeration cycle with water-lithium bromide as the working fluid pair. Energy analysis of an absorption system is a very complicated process mainly because of the limited experimental data and analytical functions required for calculating the thermodynamic properties of fluid pairs, which usually involves the solution of complex differential equations. Instead of complex differential equation and limited experimental data, faster and simpler solutions were obtained by using equations derived from the ANN model. As seen from the results obtained, the calculated thermodynamic properties are within acceptable results. Thermodynamic properties of each point in the cycle are calculated using related equations of the state. Heat flow rate of each component in the cycle and some performance parameters are calculated from the first law analysis. The results show that a high coefficient of performance value is obtained at high generator and evaporator temperatures and also at low condenser and absorber temperatures.

The objective of this work is to model an artificial neural network (ANN) to predict the value of specific heat capacity of working fluid LiBr-H2O used in vapour absorption refrigeration systems. A feed forward back propagation algorithm is used for the network, which is most popular for ANN. The consistence between experimental and ANNâ??s approach result was achieved by a mean relative error -0.00573, sum of the squares due to error0.00321, coefficient of multiple determination R-square 0.99961and root mean square error 0.01573 for test data. These results had been achieved in Matlab environment and the use of derived equations in any programmable language for deriving the specific heat capacity of LiBr-H2O solution.

The objective of this work is to compare performances of three training functions (TRAINBR, TRAINCGB and TRAINCGF) used for training neural network for predicting the value of the specific heat capacity of working fluid, LiBrH2O, used in vapour absorption refrigeration system. The comparison is shown on the basis of percentage relative error, coefficient of multiple determination R-square, root mean square error and sum of the square due to error.