A review on feedstocks, production processes, and yield for different generations of biodiesel

Chemical compositions, properties, and standards for different generation biodiesels: A review

A comprehensive review of physicochemical properties, production process, performance and emissions characteristics of 2nd generation biodiesel feedstock: Jatropha curcas

Energy, exergy, and emission analysis of a hydroxyl fueled compression ignition engine under dual fuel mode

Project selection has a critical role in the successful execution of the lean six sigma (LSS) program in any industry. The poor selection of LSS projects leads to limited results and diminishes the credibility of LSS initiatives. For this reason, in this article, we propose a method for the assessment and effective selection of LSS projects. Intuitionistic fuzzy sets based on the weighted average were adopted for aggregating individual suggestions of decision makers. The weights of selection criteria were computed using entropy measures and the available projects are prioritized using the multiattribute decision making approach, i.e., modified TOPSIS and VIKOR. The proposed methodology is validated through a case example of the LSS project selection in a manufacturing organization. The results of the case study reveal that out of eight LSS projects, the assembly section (A8) is the best LSS project. A8 is the ideal LSS project for swift gains and manufacturing sustainability. The robustness and reliability of the obtained results are checked through a sensitivity analysis. The proposed methodology will help manufacturing organizations in the selection of the best opportunities among complex situations, results in sustainable development. The engineering managers and LSS consultants can also adopt the proposed methodology for LSS project selections.

Lean Six Sigma project selection in a manufacturing environment using hybrid methodology based on intuitionistic fuzzy MADM approach

Excessive use of diesel engines and continuous increase in environmental pollution has drawn the attention of researchers in the area of the compression ignition engine. In this research article, an innovative investigation of the nonroad modified diesel engine is reported with the effective use of the hybrid Entropy-VIKOR approach. Hence, it becomes necessary to prioritize and optimize the performance defining criteria, which provides higher BTE along with lower emission simultaneously. The engine load, injection timing (Inj Tim), injection pressure (Inj Pre), and compression ratio (Com R) were selected as engine operating parameters for experimentation at the constant speed of 1500 rpm engine. The effect on engine performance parameters (BTE and BSEC) and emission (carbon monoxide (CO), total oxide of carbon (TOC), oxides of nitrogen (NOx), hydrocarbon (HC), and smoke) was studied experimentally. The optimum results were observed at load 10.32 kg, Inj Tim 20 deg btdc, Inj Pre 210 bar, and Com R 21:1 at which highest BTE of 22.24% and lowest BSEC of 16,188.5 kJ/kWh were obtained. Hybrid entropy-VIKOR approach was applied to establish the optimum ranking of the nonroad modified diesel engine. The experimental results and numerical simulation show that optimizing the engine operating parameters using the entropy-VIKOR multicriteria decision-making (MCDM) technique is applicable.

Shayam Lal Soni

Papers

Characterization of the non-road modified diesel engine using a novel Entropy-VIKOR approach: Experimental investigation and numerical simulation