AI-driven optimization of ethanol-powered internal combustion engines in alignment with multiple SDGs: A sustainable energy transition

With the escalating requirement for global sustainable energy solutions and the complexities linked with the complete transition to new technologies, internal combustion engines (ICEs) powered with biofuels like ethanol are gaining significance over time. However, problems linked to the performance and emissions of such ICEs necessitate accurate prediction and optimization. The study employed the integration of artificial neural networks (ANN) and multi-level historical design of response surface methodology (RSM) to address these challenges in alignment with the Sustainable Development Goals (SDGs). A single-cylinder spark ignition (SI) engine powered with ethanol-gasoline blends at different loads and speeds was used to gather data. Among six initially trained ANN models, the most efficient model with a regression coefficient (R2) of 0.9952 (training), 0.98579 (validation), 0.98847 (testing), and 0.99307 (overall) was employed to predict outputs such as brake power, brake specific fuel consumption (BSFC), brake thermal energy (BTE), concentration of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen NOx. Predicted outputs were optimized by incorporating RSM. On implementing optimized conditions, it was observed that BP and BTE increased by 19.9%, and 29.8%, respectively. Additionally, CO, and HC emissions experienced substantial reductions of 28.1%, and 40.6%, respectively. This research can help engine producers and researchers make refined decisions and achieve improved performance and emissions. The study directly supports SDG 7, SDG 9, SDG 12, SDG 13, and SGD 17, which call for achieving affordable, clean energy, sustainable industrialization, responsible consumption, and production, taking action on climate change, and partnership to advance the SDGs as a whole respectively

Applications of Artificial Neural Networks in Biofuels

This chapter is focused on the application of artificial neural networks (ANNs) in the development of alternative methods for biofuel quality issues. At first, the advances and the proliferation of models and architectures of artificial neural networks are highlighted in the text by the characteristics of robustness and fault tolerance, learning capacity, uncertain information processing and parallelism, which allow the application in problems of complex nature. In this scenario, biofuels are contextualized and focused on issues of quality control and monitoring. Therefore, this chapter leads to a study of prediction and/or classification of biofuels quality parameters by the description of published works on the topic under discussion. Afterwards, a case study is performed to demonstrate, in a practical way, the steps and procedures to build alternative models for predicting the oxidative stability of biodiesel. The procedure goes from the processing of the data obtained by the near infrared until the evaluation of the alternative method developed by the neural network. In addition, some evaluation parameters are described for the assessment of the alternative method built. As a result, the feasibility and practicality of the application of neural networks to the quality of biofuels are proven

Predicting engine performance and exhaust emissions of a spark ignition engine fuelled with 2-butanol-gasoline blends using RSM and ANN models

Experimental investigation in engine testing using alternative fuels always subjected to more engine operation, time-consuming and require expensive cost of materials. For these reasons, this study is aimed to predict the engine performance and exhaust emissions using 2-butanol-gasoline blended fuels with percentage volume ratios of 5:95 (GBu5), 10:90 (GBu10) and 15:85 (GBu15) of gasoline to 2-butanol, respectively, operated in a four-cylinder, four-stroke port fuel 4G93 Mitsubishi spark ignition engine at 30%, 50% and 70% of throttle position using artificial neural network and response surface methodology techniques. Based on the experimental investigation, at 30%, 50% and 70% of throttle position, 2-butanol–gasoline blended fuels indicated an improvement in engine brake power, brake torque and brake thermal efficiency with increasing 2-butanol content in the gasoline fuels. The engine performance indicated improvement in brake power, brake torque and brake thermal efficiency in the average of 2 to 15% and 0.2% to 1.5%,respectively, for all of the tested throttle position with respect to increasing the 2-butanol content in the gasoline fuel. For exhaust emissions, it was recorded that, a significant decreased of NOx, CO, CO2 and HC for GBu5, GBu10 and GBu15, by an average of 7.1%, 13.7%, and 19.8% than G100, respectively, over a speed range of 1000 to 4000 RPM. Other emission contents indicate lower CO and HC but higher CO2 from 2500 to 4000 RPM for the blended fuels. The engine speeds, 2-butanol blended fuels and engine throttle position and results from the engine performance and exhaust emissions characteristics was then used as the input and output for the for the artificial neural network and response surface methodology. Based on the RSM model, performance characteristics revealed that the increment of 2-butanol in the blended fuels lead to the increasing trends of brake power, brake torque and brake thermal efficiency. Nonetheless, a marginally higher brake specific fuel consumption was observed. Furthermore, the RSM model suggests that the presence of 2-butanol exhibits a decreasing trend of NOx, CO, and HC, however, a higher trend was observed for CO2 exhaust emissions, which are in accordance with the experimental results. Meanwhile, for ANN it was shown that the two hidden layer ANN model trained with the tansig-logsig activation function combination yields the best correlation coefficient, R at a value of 0.9995 against other activation function combinations evaluated. However, to attain a higher fidelity prediction model, all the configurations are further assessed by additional statistical error and correlation metrics, namely Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Theil U2, Nash-Sutcliffe Efficiency (NSE) and Kling–Gupta Efficiency (KGE). Following the evaluation, the best activation function combination for the brake power, BSFC, BTE, NOx, CO, and CO2 ANN predictive models is the tansig-logsig configuration. As for Brake torque and HC, the tansig combination provides a better prediction. It can be conclusively shown from the study that the developed ANN models have a higher predictive accuracy as compared to the RSM model

ADAPTIVE MODEL BASED COMBUSTION PHASING CONTROL FOR MULTI FUEL SPARK IGNITION ENGINES

This research describes a physics-based control-oriented feed-forward model, combined with cylinder pressure feedback, to regulate combustion phasing in a spark-ignition engine operating on an unknown mix of fuels. This research may help enable internal combustion engines that are capable of on-the-fly adaptation to a wide range of fuels. These engines could; (1) facilitate a reduction in bio-fuel processing, (2) encourage locally-appropriate bio-fuels to reduce transportation, (3) allow new fuel formulations to enter the market with minimal infrastructure, and (4) enable engine adaptation to pump-to-pump fuel variations. These outcomes will help make bio-fuels cost-competitive with other transportation fuels, lessen dependence on traditional sources of energy, and reduce greenhouse gas emissions from automobiles; all of which are pivotal societal issues. Spark-ignition engines are equipped with a large number of control actuators to satisfy fuel economy targets and maintain regulated emissions compliance. The increased control flexibility also allows for adaptability to a wide range of fuel compositions, while maintaining efficient operation when input fuel is altered. Ignition timing control is of particular interest because it is the last control parameter prior to the combustion event, and significantly influences engine efficiency and emissions. Although Map-based ignition timing control and calibration routines are state of art, they become cumbersome when the number of control degrees of freedom increases are used in the engine. The increased system complexity motivates the use of model-based methods to minimize product development time and ensure calibration flexibility when the engine is altered during the design process. A closed loop model based ignition timing control algorithm is formulated with: 1) a feed forward fuel type sensitive combustion model to predict combustion duration from spark to 50% mass burned; 2) two virtual fuel property observers for octane number and laminar flame speed feedback; 3) an adaptive combustion phasing target model that is able to self-calibrate for wide range of fuel sources input. The proposed closed loop algorithm is experimentally validated in real time on the dynamometer. Satisfactory results are observed and conclusions are made that the closed loop approach is able to regulate combustion phasing for multi fuel adaptive SI engines

Experimental and Numerical Analysis of Ethanol Fueled HCCI Engine

Presently, the research on the homogeneous charge compression ignition (HCCI) engines has gained importance in the field of automotive power applications due to its superior efficiency and low emissions compared to the conventional internal combustion (IC) engines. In principle, the HCCI uses premixed lean homogeneous charge that auto-ignites volumetrically throughout the cylinder. The homogeneous mixture preparation is the main key to achieve high fuel economy and low exhaust emissions from the HCCI engines. In the recent past, different techniques to prepare homogeneous mixture have been explored. The major problem associated with the HCCI is to control the auto-ignition over wide range of engine operating conditions. The control strategies for the HCCI engines were also explored. This dissertation investigates the utilization of ethanol, a potential major contributor to the fuel economy of the future. Port fuel injection (PFI) strategy was used to prepare the homogeneous mixture external to the engine cylinder in a constant speed, single cylinder, four stroke air cooled engine which was operated on HCCI mode. Seven modules of work have been proposed and carried out in this research work to establish the results of using ethanol as a potential fuel in the HCCI engine. Ethanol has a low Cetane number and thus it cannot be auto-ignited easily. Therefore, intake air preheating was used to achieve auto-ignition temperatures. In the first module of work, the ethanol fueled HCCI engine was thermodynamically analysed to determine the operating domain. The minimum intake air temperature requirement to achieve auto-ignition and stable HCCI combustion was found to be 130 °C. Whereas, the knock limit of the engine limited the maximum intake air temperature of 170 °C. Therefore, the intake air temperature range was fixed between 130-170 °C for the ethanol fueled HCCI operation. In the second module of work, experiments were conducted with the variation of intake air temperature from 130-170 °C at a regular interval of 10 °C. It was found that, the increase in the intake air temperature advanced the combustion phase and decreased the exhaust gas temperature. At 170 °C, the maximum combustion efficiency and thermal efficiency were found to be 98.2% and 43% respectively. The NO emission and smoke emissionswere found to be below 11 ppm and 0.1% respectively throughout this study. From these results of high efficiency and low emissions from the HCCI engine, the following were determined using TOPSIS method. They are (i) choosing the best operating condition, and (ii) which input parameter has the greater influence on the HCCI output. In the third module of work, TOPSIS - a multi-criteria decision making technique was used to evaluate the optimum operating conditions. The optimal HCCI operating condition was found at 70% load and 170 °C charge temperature. The analysis of variance (ANOVA) test results revealed that, the charge temperature would be the most significant parameter followed by the engine load. The percentage contribution of charge temperature and load were63.04% and 27.89% respectively. In the fourth module of work, the GRNN algorithm was used to predict the output parameters of the HCCI engine. The network was trained, validated, and tested with the experimental data sets. Initially, the network was trained with the 60% of the experimental data sets. Further, the validation and testing of the network was done with each 20% data sets. The validation results predicted that, the output parameters those lie within 2% error. The results also showed that, the GRNN models would be advantageous for network simplicity and require less sparse data. The developed new tool efficiently predicted the relation between the input and output parameters. In the fifth module of work, the EGR was used to control the HCCI combustion. An optimum of 5% EGR was found to be optimum, further increase in the EGR caused increase in the hydrocarbon (HC) emissions. The maximum brake thermal efficiency of 45% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 10 ppm and 0.61% respectively. In the sixth module of work, a hybrid GRNN-PSO model was developed to optimize the ethanol-fueled HCCI engine based on the output performance and emission parameters. The GRNN network interpretive of the probability estimate such that it can predict the performance and emission parameters of HCCI engine within the range of input parameters. Since GRNN cannot optimize the solution, and hence swarm based adaptive mechanism was hybridized. A new fitness function was developed by considering the six engine output parameters. For the developed fitness function, constrained optimization criteria were implemented in four cases. The optimum HCCI engine operating conditions for the general criteria were found to be 170 °C charge temperature, 72% engine load, and 4% EGR. This model consumed about 60-75 ms for the HCCI engine optimization. In the last module of work, an external fuel vaporizer was used to prepare the ethanol fuel vapour and admitted into the HCCI engine. The maximum brake thermal efficiency of 46% was found for 170 °C charge temperature at 80% engine load. The NO emission and smoke emission were found to be below 5 ppm and 0.45% respectively. Overall, it is concluded that, the HCCI combustion of sole ethanol fuel is possible with the charge heating only. The high load limit of HCCI can be extended with ethanol fuel. High thermal efficiency and low emissions were possible with ethanol fueled HCCI to meet the current demand

Modeling the effect of blending multiple components on gasoline properties

Global CO2 emissions reached a new historical maximum in 2018 and transportation sector contributed to one fourth of those emissions. Road transport industry has started moving towards more sustainable solutions, however, market penetration for electric vehicles (EV) is still too slow while regulation for biofuels has become stricter due to the risk of inflated food prices and skepticism regarding their sustainability. In spite of this, Europe has ambitious targets for the next 30 years and impending strict policies resulting from these goals will definitely increase the pressure on the oil sector to move towards cleaner practices and products. Although the use of biodiesel is quite extended and bioethanol is already used as a gasoline component, there are no alternative drop-in fuels compatible with spark ignition engines in the market yet. Alternative feedstock is widely available but its characteristics differ from those of crude oil, and lack of homogeneity and substantially lower availability complicate its integration in conventional refining processes. This work explores the possibility of implementing Machine Learning to develop predictive models for auto-ignition properties and to gain a better understanding of the blending behavior of the different molecules that conform commercial gasoline. Additionally, the methodology developed in this study aims to contribute to new characterization methods for conventional and renewable gasoline streams in a simpler, faster and more inexpensive way. To build the models included in this thesis, a palette with seven different compounds was chosen: n-heptane, iso-octane, 1-hexene, cyclopentane, toluene, ethanol and ETBE. A data set containing 243 different combinations of the species in the palette was collected from literature, together with their experimentally measured RON and/or MON. Linear Regression based on Ordinary Least Squares was used as the baseline to compare the performance of more complex algorithms, namely Nearest Neighbors, Support Vector Machines, Decision Trees and Random Forest. The best predictions were obtained with a Support Vector Regression algorithm using a non-linear kernel, able to reproduce synergistic and antagonistic interaction between the seven molecules in the samples

Experimental Modeling of NOx and PM Generation from Combustion of Various Biodiesel Blends for Urban Transport Buses

Biodiesel has diverse sources of feedstock and the amount and composition of its emissions vary significantly depending on combustion conditions. Results of laboratory and field tests reveal that nitrogen oxides (NOx) and particulate matter (PM) emissions from biodiesel are influenced more by combustion conditions than emissions from regular diesel. Therefore, NOx and PM emissions documented through experiments and modeling studies are the primary focus of this investigation. In addition, a comprehensive analysis of the feedstock-related combustion characteristics and pollutants are investigated. Research findings verify that the oxygen contents, the degree of unsaturation, and the size of the fatty acids in biodiesel are the most important factors that determine the amounts and compositions of NOx and PM emissions

Enhancing Performance and Reducing Emissions in Natural Gas Aspirated Engines through Machine Learning Algorithm

In an era where the global energy landscape is increasingly defined by the dual imperatives of efficiency and sustainability, the natural gas sector stands at a crucial juncture. The engines powering this sector, especially Natural Gas Fired Reciprocating Engines (NGFRE), are well known for their performance as well as considerable emissions, posing a stark challenge to environmental sustainability goals. This thesis addresses this pivotal issue, presenting a machine learning-based solution to optimize NGFRE performance while substantially reducing their environmental footprint. The research is anchored in an experimental framework involving the AJAX DPC-81 engine compressor, evaluated across a spectrum of operational loads from 40% to 75%. The study leverages an extensive array of sensors to collect detailed real-time data on engine performance, emissions, and vibration parameters. Central to the methodology is the strategic adjustment of the Air Management System (AMS), varying air/fuel ratio to explore their impact on engine dynamics and emissions. The study also incorporates a comprehensive vibration analysis, providing critical insights into the engine's operational stability under different load conditions. Machine Learning (ML) techniques, including Linear Regression, Artificial Neural Networks (ANN), and Support Vector Machines (SVM), are integrated with a Programmable Logic Controller (PLC). This integration not only facilitates a nuanced analysis of the collected data but also enables the accurate prediction of engine performance, paving the way for real-time adaptive control systems. The findings of this research are both revealing and impactful. A notable instance is observed at a 40% engine load with a 70% bypass valve opening, where emissions of methane (CH4) plummet by 64%, nitrogen oxides (NOx) by 52%, and Volatile Organic Compounds (VOC) by 50%. This substantial decrease highlights the effectiveness of the ML-driven approach in curbing harmful emissions. Further, the study unveils the manipulation of the bypass valve position can lead to enhanced fuel efficiency and improved engine stability. For example, at a 75% engine load, the research demonstrates that optimal emission reduction is achieved with a mere 10% bypass valve opening, illuminating the delicate interplay between engine load parameters and environmental emissions. In conclusion, the study demonstrates the effectiveness of ML in enhancing NGFRE performance. It sets a foundation for developing intelligent engine systems that can self-adjust for optimal performance and minimal environmental impact, forging a path to a future where the two are seamlessly integrated

Combustion Phasing Modeling for Control of Spark-Assisted Compression Ignition Engines

Substantial fuel economy improvements for light-duty automotive engines demand novel combustion strategies. Low temperature combustion (LTC) demonstrates potential for significant fuel efficiency improvement; however, control complexity is an impediment for real-world transient operation. Spark-assisted compression ignition (SACI) is an LTC strategy that applies a deflagration flame to generate sufficient energy to trigger autoignition in the remaining charge. For other LTC strategies, control of autoignition timing is difficult as there is no direct actuator for combustion phasing. SACI addresses this challenge by using a spark plug to initiate a flame that then triggers autoignition in a significant portion of the charge. The flame propagation phase limits the rate of cylinder pressure increase, while autoignition rapidly completes combustion. High dilution is generally required to maintain production-feasible reaction rates. This high dilution, however, increases the likelihood of flame quench, and therefore potential misfires. Mitigating these competing constraints requires careful mixture preparation strategies for SACI to be feasible in production. Operating a practical engine within this restrictive regime is a key modeling and control challenge. Current models are not sufficient for control-oriented work such as calibration optimization, transient control strategy development, and real-time control. To resolve the modeling challenge, a fast-running cylinder model is developed and presented in this work. It comprises of five bulk gas states and a fuel stratification model comprising of ten equal-mass zones within the cylinder. The zones are quasi-dimensional, and their state varies with crank angle to capture the effect of fuel spray and mixing. For each zone, combustion submodels predict flame propagation burn duration, autoignition phasing, and the concentration of oxides of nitrogen. During the development of the combustion submodels, both physics-based and data-driven techniques are considered. However, the best balance between accuracy and computational expense leads to the nearly exclusive selection of data-driven techniques. The data-driven models are artificial neural networks (ANNs), trained to an experimentally-validated one-dimensional (1D) engine reference model. The simplified model matches the reference 1D engine model with an R2 value of 70‒96% for key combustion parameters. The model requires 0.8 seconds to perform a single case, a 99.6% reduction from the reference 1D engine model. The reduced model simulation time enables rapid exploration of the control space. Over 250,000 cases are evaluated across the entire range of actuator positions. From these results, a transient-capable calibration is formulated. To evaluate the strength of the steady-state calibration, it is operated over a tip-in and tip-out. The response to the transients required little adjustment, suggesting the steady-state calibration is robust. The model also demonstrates the capability to adapt in-cylinder state and spark timing to offset combustion phasing disturbances. This positive performance suggests the candidate model developed in this work retains sufficient accuracy to be beneficial for control-oriented objectives. There are four contributions of this research: 1) a demonstration of the impact of combustion fundamentals on SACI combustion, 2) an identification of suitable techniques for data-driven modeling, 3) a quasi-dimensional fuel stratification model for radially-stratified engines, and 4) a comprehensive cylinder model that maintains high accuracy despite substantially reduced computational expense

Evaluation of combustion characteristics, performance and exhaust emission for diesel fuel with various type nano particle blends

This research investigates the effect of nanoparticles (aluminium oxides, carbon nanotubes and silicone oxide) blend in diesel fuel on physio-chemical properties, combustion characteristics, performance and exhaust emission of a four-stroke single cylinder engine with direct injection. Nanoparticle is widely use as additive due to its high surface to volume ratio thus have better thermal properties. But most the past literatures only focus on single nanoparticle instead of mixture of nanoparticle. So this research will utilise response surface methodology to determine the best blend ratio of the three nanoparticles. Beside that nanoparticle is very expensive to produce, so optimal concentration of each nanoparticle in diesel fuel was determined by using Box-Behnken’s response surface methodology to maximise the performance and reduce emission of diesel fuel. The nanoparticles were dispersed in a dosage of 25, 50 and 100 ppm in pure diesel fuel using ultrasonic processor for 30 minutes. Aluminium oxides (Al2O3) and carbon nanotubes (CNT) fuel blends show reduction of kinematic viscosity by 9.6 to 18.8 % compared to diesel fuel. Meanwhile, the calorific value increased by 4.12 % with CNT blends. However, the cetane number was remain with additional of the nanoparticles. The blend fuels were experimentally tested with YANMAR TF120M single cylinder four-stroke diesel engine at engine load of 0, 25, 50, 75 and 100 % of 5.9 bar brake main effective pressure (BMEP) at a constant 1500 rpm engine speed. The results revealed that the brake specific fuel consumption (BSFC) showed reduction up to 19.8 % while 18.8 % enhancement shown in brake thermal efficiency (BTE). Next, the model from response surface methodology (RSM) was used for optimization with an objective of minimizing the fuel consumption, CO, CO2, NOX and HC emissions. Utilizing this approach, the blend fuel with 100 ppm Al2O3 and 100 ppm CNT with 79.13 ppm SiO2 was considered to deliver optimum emission and performance characteristics with a maximum desirability of 0.9846 at 25% engine load