Anand Krishnasamy

Reactivity Controlled Compression Ignition (RCCI) is a promising, high-efficiency, clean combustion mode for diesel engines. One of the significant limitations of RCCI is its higher unburned hydrocarbon (HC) and carbon monoxide (CO) emissions compared to conventional diesel combustion. After-treatment control of HC and CO emissions is difficult to achieve in RCCI because of lower exhaust gas temperatures associated with the low-temperature combustion (LTC) mode of operation. The present study involves combined experimental and computational fluid dynamic (CFD) investigations to develop the most effective HC and CO control strategy for RCCI. A production light-duty diesel engine is modified to run in RCCI mode by introducing electronic port fuel injection with the replacement of mechanical injectors by the CRDI system. Experimental data were obtained using diesel as HRF (High reactive fuel) and gasoline as LRF (low reactive fuel). The combustion simulation was performed using the CONVERGE 3D CFD tool. A reduced PRF mechanism was used where iso-octane represents gasoline and n-heptane as diesel. After validation of engine combustion, performance, and emission parameters, parametric investigations were carried out to investigate the effects of HRF's start of injection timing, premixed energy share, and intake charge temperature on combustion and exhaust emissions. The results obtained from both CFD and experiment show that the start of injection and intake charge temperature significantly influence combustion phasing, while the premixed ratio controls mixture reactivity and combustion quality. The blending ratio of high HRF to LRF governs reactivity stratification, which controls the magnitude of low and high-temperature heat release, combustion phasing and combustion duration. Controlling the amount of LRF and HRF in direct injection (DI) allows for shifting the heat release rate, which modifies combustion phasing and rate of pressure rise. Multiple injection strategies using double pulse helped reduce CO formation and achieve better control over combustion parameters with improved efficiency. By varying IVC temperature, optimizing SOI timing using a double injection strategy up to 18.57%, 25.5% reduction in CO and 93.68% drop in HC emissions, 3.7% reduction in soot are obtained in RCCI compared to the baseline case.

The present research explores the application of biodiesel fuel in a stationary agricultural engine operated under the Homogenous charge compression ignition (HCCI) mode. To achieve HCCI combustion, a fuel vaporizer and a high-pressure port fuel injection system are employed to facilitate rapid evaporation of the biodiesel fuel. The low volatility of biodiesel is one of the significant shortcomings, which makes it inevitable to use a fuel vaporizer at 380oC. Consequently, the charge temperature is high enough to promote advanced auto-ignition. Further, the high reactivity of biodiesel favors early auto-ignition of the charge. Besides, biodiesel exhibits a faster burn rate due to its oxygenated nature. The combined effect of advanced auto-ignition and faster burn rate resulted in a steep rise in the in-cylinder pressures, leading to abnormal combustion above 20% load. Diluting the charge reduces reactivity and intake oxygen concentration, facilitating load extension. This study explores two charge diluents: recirculated exhaust gas (EGR) and water vapor induction into the intake manifold. With EGR, the maximum load is 40%, whereas 46% of the rated load could be achieved with water vapor induction. The maximum load could be extended up to 50% with the combined dilution using EGR and water vapor. The charge dilution required with water vapor is less than that of EGR. Additionally, charge dilution with water vapor results in better thermal efficiency, fuel economy, and lower emissions than EGR. Overall, the present study confirms the functionality and feasibility of biodiesel in HCCI engines, demonstrating the applicability of charge dilution to address its significant shortcoming of a narrow stable combustion regime.

Various feedstocks can be employed for biodiesel production, leading to considerable variation in composition and engine fuel characteristics. Using biodiesels originating from diverse feedstocks introduces notable variations in engine characteristics. Therefore, it is imperative to scrutinize the composition and properties of biodiesel before deployment in engines, a task facilitated by predictive models. Additionally, the international commercialization of biodiesel fuel is contingent upon stringent regulations. The traditional experimental measurement of biodiesel properties is laborious and expensive, necessitating skilled personnel. Predictive models offer an alternative approach by estimating biodiesel properties without depending on experimental measurements. This research is centered on building models that correlate mid-infrared spectra of biodiesel and critical fuel properties, encompassing kinematic viscosity, cetane number, and calorific value. The novelty of this investigation lies in exploring the suitability of support vector machine (SVM) regression, a burgeoning machine learning algorithm, for developing these models. Hyperparameter optimization for the SVM models was conducted using the grid search method, Bayesian optimization, and gray wolf optimization algorithms. The resultant SVM models exhibited a noteworthy reduction in mean absolute percentage error (MAPE) for the prediction of biodiesel viscosity (3.1%), cetane number (3%), and calorific value (2.1%). SVM regression, thus, emerges as a proficient machine learning algorithm capable of establishing correlations between the mid-infrared spectra of biodiesel and its properties, facilitating the reliable prediction of biodiesel characteristics.

Although diesel after-treatment techniques demonstrate a substantial decrease in tailpipe emissions, the primary goal continues to be attaining nearly zero emissions to comply with current regulatory requirements and foreseeable imminent rigorous regulations. To achieve this objective, engine combustion systems and fuel formulations require fine-tuning. Taking insights from the recent literature, this review examines various processes, including homogeneous in situ methods, the incorporation of fuel-borne catalysts, and the use of biofuels. Despite progress in in-cylinder pollution mitigation techniques like low-temperature combustion, which exhibits substantial reductions in oxides of nitrogen (NOx) and soot emissions, it is crucial to consistently advance technology to conform to evolving emission regulations and environmental concerns. A discussion is presented regarding the advantages and disadvantages of the homogeneous charge compression ignition (HCCI) mode and their potential for the future, focusing on biodiesel-fueled HCCI engines. The review assesses the effectiveness of thermal management strategies and engine design modifications that extend the operational range of HCCI engines powered by biodiesel in light of the inherent limitation of a restricted engine operating range. The review critically examines the merits and demerits of biofuels and HCCI systems and provides an essential analysis of current after-treatment approaches. With an imperative focus, the primary aim of this review is to ascertain modern catalysts designed explicitly for use in contemporary combustion systems. An exhaustive examination of the progress made in diesel oxidation catalysts, selective catalytic reduction techniques, lean NOx traps, diesel particulate filters, and catalyst regeneration is presented. The concluding remarks analyze the catalytic characteristics necessary for smooth incorporation with modern technological developments in various combustion systems.

Reinforcement learning (RL) is a computational approach to understanding and automating goal-directed learning and decision-making. The difference from other computational approaches is the emphasis on learning by an agent from direct interaction with its environment to achieve long-term goals [1]. In this work, the RL algorithm was implemented using Python. This then enables the RL algorithm to make decisions to optimize the output from the system and provide real-time adaptation to changes and their retention for future usage. A diesel engine is a complex system where a RL algorithm can address the NOx-soot emissions trade-off by controlling fuel injection quantity and timing. This study used RL to optimize the fuel injection timing to get a better NO-soot trade-off for a common rail diesel engine. The diesel engine utilizes a pilot-main and a pilot-main-post-fuel injection strategy. Change of fuel injection quantity was not attempted in this study as the main objective was to demonstrate the use of RL algorithms while maintaining a constant indicated mean effective pressure. A change in fuel quantity has a larger influence on the indicated mean effective pressure than a change in fuel injection timing. The focus of this work was to present a novel methodology of using the 3D combustion data from analysis software in the form of a functional mock-up unit (FMU) and showcasing the implementation of a RL algorithm in Python language to interact with the FMU to reduce the NO and soot emissions by suggesting changes to the main injection timing in a pilot-main and pilot-main-post-injection strategy. RL algorithms identified the operating injection strategy, i.e., main injection timing for a pilot-main and pilot-main-post-injection strategy, reducing NO emissions from 38% to 56% and soot emissions from 10% to 90% for a range of fuel injection strategies.

Homogeneous Charge Compression Ignition (HCCI) engine is a prospective technology that effectively utilizes net carbon-neutral biofuels to achieve ultra-low nitrogen oxides (NOx) and smoke emissions with minimized indicated specific energy consumption (ISEC). The HCCI engine characteristics vary significantly with the biofuel type, requiring an investigation for the most suitable fuel composition and properties for enhanced fuel economy and lower exhaust emissions. In the present investigation, the critical fuel parameters of molecular weight, hydrogen-to-oxygen ratio, carbon-to-oxygen ratio, research octane number, energy content, and cooling potential were chosen based on their impact on the HCCI combustion load range limits. The support vector machine (SVM) regression models were developed to predict the HCCI engine characteristics, including combustion phasing, ISEC, and regulated emissions, using the above-mentioned critical fuel parameters as inputs. Experiment data obtained by running the HCCI engine using seven biofuels blended in gasoline consisted of 147 data points that trained the SVM models. The chosen biofuels belonged to distinct oxygenated organic functional groups of alcohol, ester, ether, and ketone. The relative importance of each key fuel parameter in predicting the investigated engine parameters was estimated. The robust SVM models were used in multi-objective Pareto-search optimization to find Pareto optimal solutions. TOPSIS was used to select the best alternative among 70 Pareto-optimal solutions for minimum ISEC and regulated emissions. The optimized fuel composition in the HCCI mode reduced the ISEC by 18% and NOx by 76% than the conventional diesel combustion mode. Also, the HCCI mode produced almost zero smoke emissions (< 0.0007 g/kW-h). The optimal fuel parameters could be achieved by tuning the biofuel proportions in gasoline. The present work demonstrated that using optimized fuel composition for HCCI combustion could tackle the significant performance and emission drawbacks of conventional light-duty diesel engines.