Experimental study of butanol/biodiesel dual-fuel combustion in intelligent charge compression ignition (ICCI) mode: A systematic analysis at low load

Humanity is on the verge of setting new benchmarks in warming up the planet 4-5°C by triggering a series of cascading tripping points. Rising carbon content in the atmosphere is causing an unprecedented spike in global temperatures leading to drastic climatic changes like massive heat waves, droughts, melting of polar ice caps and a rise in the sea level. Vehicles alone are responsible for at least 12% of the total Carbon dioxide (CO2) emissions in the world. Carbon Monoxide (CO), a toxic air pollutant, is produced by the incomplete combustion of carbon-containing fuels like gasoline in automobiles and its emissions are responsible for a wide range of health problems like headache, nausea, vomiting, and dizziness. Nitrogen Oxides (NOx) emissions produced by motor vehicles are instrumental in causing smog, acid rain which can lead to adverse health effects like respiratory disorders, heart problems etc. Alternative fuels will act as a "life jacket" to save humanity from drowning in this ocean of its design. Biodiesel is a "carbon neutral" fuel that can be blended with diesel and used in driving compression ignition engines. In the study engine performance in terms of brake thermal efficiency (BTE), brake specific fuel consumption (BPSC) and CO, HC, CO2, and NOx emissions are optimized for Kusum Oil Ethyl Ester (KOEE) with butanol blend using Taguchi's method, to obtain an optimized dataset of the input parameters viz. engine rpm, fuel properties and lower heating value of fuel which are then fed into the two prediction techniques viz. Artificial Neural Network (ANN) and multiple regression. These techniques are also ranked in terms of precision, based on generated errors.

Optimization of engine performance parameters and exhaust emissions in compression ignition engine fueled with biodiesel-alcohol blends using taguchi method, multiple regression and artificial neural network

The effect of the addition of different proportions of silver (Ag) nanoparticles and alcohols in milk scum oil methyl ester on the performance of engine and emission are studied. B20 blend is added with 5% of ethanol, n-butanol, and iso-butanol as ternary additives for the experimental analysis from no load to full load. Furthermore, at a fixed load, operating conditions such as injection pressure (12 and 15 bar) and injection timing (23° and 26°) are varied without and with the addition of 0.8 vol% of Ag (silver) nanoparticles to the fuel blends. Also, the concentrations of Ag nanoparticles are increased from 0.2 to 1 vol% and comparisons are made with diesel and B60 blend. Mathematical models are developed for selected features of engine performance which fits with the experimental values for the purpose of optimization using the Dragon fly algorithm (DA) by considering these models as the objective functions. The concentration of nanoparticles lowers the BSFC significantly and helps in reducing the emission with an increased percentage. Using full biodiesel, 16.6% reduction in BTE was obtained, while use of alcohols prevented this reduction approximately by 5%. A highest of 4.6% improvement was obtained with the addition of Ag nanoparticles. 4.5% reduction in HC and 13% in NOx emission using nanoparticles are obtained. The DA algorithm provided the same optimized value at the end of 30 iterations in different cycles of execution. Nanoparticle addition and use of pressure in the range of 20 bar gives the lowest emission from the engine.

Blends of scum oil methyl ester, alcohols, silver nanoparticles and the operating conditions affecting the diesel engine performance and emission: an optimization study using Dragon fly algorithm

A Comprehensive Review of Performance, Combustion, and Emission Characteristics of Biodiesel Blend with Nanoparticles in Diesel Engines

In the broad framework of degradation assessment of bearing, the final objectives of bearing condition monitoring is to evaluate different degradation states and to estimate the quantitative analysis of degree of performance degradation. Machine learning classification matrices have been used to train models based on health data and real time feedback. Diagnostic and prognostic models based on data driven perspective have been used in the prior research work to improve the bearing degradation assessment. Industry 4.0 has required the research in advanced diagnostic and prognostic algorithm to enhance the accuracy of models. A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model. Review work demonstrates the comparisons among the available state-of-the-art methods. In the end, unexplored research technical challenges and niches of opportunity for future researchers are discussed. Highlights Abstract

http://www.ein.org.pl/sites/default/files/2021-02-20.pdf

Degradation assessment of bearing based on machine learning classification matrix

Performance Emissions and Combustion Analysis of CI Engine Using Ethyl Ester Kusum Oil and Butanol Blends

Rolling element bearing is one of the most critical components of rotating machinery. Its failure can be catastrophic and often results in both human and material losses. This paper presents a machine learning model to predict the wear process phenomena and remaining useful life of a bearing element using classification and regression techniques respectively. An algorithm is developed to recognize the underlying mapping function directly from the data using machine learning principles. Pearson correlation methodology is used to track the important features associated with the evolution of wear and understand its progression. Further, backward elimination technique with ordinary least squares regression results was used to track features for predicting the remaining useful life. The proposed approach is illustrated on a bearing failure data set from the national aeronautics and space agency. This study will be useful in forecasting the fault status of the bearing before it causes any major loss.

Predicting the Remaining Useful Life of Ball Bearing Under Dynamic Loading Using Supervised Learning

Small-scale industries face a lot of problems like lack of skilled manpower, machinery and equipment, inadequate credit assistance, absence of adequate infrastructure, etc. Consequently, they are unable to compete with the quality products delivered to the consumer by well-established industries. Hence, a yardstick is required to check the manufacturing techniques, adopted standards and finished products. Green lean with six sigma methodology has been suggested in this research for improving manufacturing processes and reducing production cost for a product manufactured by a small-scale industry. This work aims at minimizing losses incurred during manufacturing processes in a small-scale industry. The concept of green is implemented through material flow cost accounting, which acts as a diagnostic accounting method. Further, lean six sigma has been used to identify causes and propose remedies for the losses that were identified in the former technique.

Integrated Approach of Green Lean with Six Sigma for Improving Quality Issues in Small-Scale Industries

The automobile industry has faced a metamorphosis over the last three decades. There has been a shift from the scenario of a manufacturer’s market to a consumer’s market. The voice of the customer is put at priority even while deciding any specific model of a car. The bias created by the consumer towards one brand and the Indian mentality towards vehicular safety is a matter to investigate. This paper proposes a strategy for analyzing choices of consumers. Multi criteria decision-making tools, viz., TOPSIS, AHP and CROSSTAB ANALYSIS have been applied. Data of gender, age, profession and segment of the car collected in the survey is used as input parameters while making the purchase behaviour model. This analysis helps car manufacturers to incorporate the features that have been preferred by consumers and thus value would be created among them. Thereby the manufacturing firm would be able to improve its profit margins and strengthen its market shares. Simultaneously the consumer would be able to choose from a wide range of options.

Tanmay Agarwal

Papers

Performance Emissions and Combustion Analysis of CI Engine Using Ethyl Ester Kusum Oil and Butanol Blends

Predicting the Remaining Useful Life of Ball Bearing Under Dynamic Loading Using Supervised Learning

Integrated Approach of Green Lean with Six Sigma for Improving Quality Issues in Small-Scale Industries

Consumer Behaviour Analysis for Purchasing a Passenger Car in Indian Context