Analytical research of artificial intelligent models for machining industry under varying environmental strategies: An industry 4.0 approach

Seraj, Mohammad; Khan, Osama; Khan, Mohd Zaheen; Parvez, Mohd; Bhatt, Bhupendra Kumar; Ullah, Amaan; Alam, Md Toufique

doi:10.1016/j.susoc.2022.01.006

Cited by 24 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance and exhaust attributes were estimated for the developed couple mixtures on a petro-diesel engine, generating a data set based on various nanoparticle proportions for various blend fractions, engine loads, and injection pressures. Subsequent segments comprise a comprehensive explanation of the steps followed in information prediction …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS–GA–HSA Approach

Khan

Alam³

et al. 2023

ACS Omega

Self Cite

View full text Add to dashboard Cite

Since the discovery of petrol-based products, a surge in energy-requiring equipment has been established across the world. Recent depletion of the existing crude oil resources has motivated researchers to opt for and analyze potential fuels that could potentially provide a cost-effective and sustainable solution. The current study selects a waste plant known as Eichhornia crassipes through which biodiesel is generated, and its blends are tested in diesel engines for feasibility. Different models using soft computing and metaheuristic techniques are employed for the accurate prediction of performance and exhaust characteristics. The blends are further mixed with nanoadditives, thereby exploring and comparing the changes in performance characteristics. The input attributes considered in the study comprise engine load, blend percentage, nanoparticle concentration, and injection pressure, while the outcomes are brake thermal efficiency, brake specific energy consumption, carbon monoxide, unburnt hydrocarbon, and oxides of nitrogen. Models were further ranked and chosen based on their set of attributes using the ranking technique. The ranking criteria for models were based on cost, accuracy, and skill requirement. The ANFIS harmony search algorithm (HSA) reported a lower error rate, while the ANFIS model reported the lowest cost. The optimal combination achieved was 20.80 kW, 2.48047, 150.501 ppm, 4.05025 ppm, and 0.018326% for brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC), and carbon monoxide (CO), respectively, thereby furnishing better results than the adaptive neuro-fuzzy interface system (ANFIS) and the ANFIS−genetic algorithm model. Henceforth, integrating the results of ANFIS with an optimization technique with the harmony search algorithm (HSA) yields accurate results but at a comparatively higher cost.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Subsequent segments comprise a comprehensive explanation of the steps followed in information prediction. 39 …”

Section: Methodsmentioning

confidence: 99%

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS–GA–HSA Approach

Khan

Alam³

et al. 2023

ACS Omega

Self Cite

View full text Add to dashboard Cite

show abstract

“…The process begins with the treatment of metal particles with distilled water, and it is followed by the transfer of the mixture into an ultrasonic reactor for a predetermined period of time. During the course of this investigation, aluminium oxide and copper oxide are weighed before being mixed with water that has been distilled, and then the mixture is subjected to an ultrasonic reactor operating at 90–100 kHz for twenty minutes 24 . Nanoparticles of Al 2 O 3 and CuO that were employed in the study have a size of thirty nanometers (nm).…”

Section: Methodsmentioning

confidence: 99%

Modelling of compression ignition engine by soft computing techniques (ANFIS-NSGA-II and RSM) to enhance the performance characteristics for leachate blends with nano-additives

Khan,

Parvez,

Kumari

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Integrating nanoparticles in waste oil-derived biodiesel can revolutionize its performance in internal combustion engines, making it a promising fuel for the future. Nanoparticles act as combustion catalysts, enhancing combustion efficiency, reducing emissions, and improving fuel economy. This study employed a comprehensive approach, incorporating both quantitative and qualitative analyses, to investigate the influence of selected input parameters on the performance and exhaust characteristics of biodiesel engines. The focus of this study is on the potential of using oils extracted from food waste that ended up in landfills. The study's results are analysed and compared with models created using intelligent hybrid prediction approaches including adaptive neuro-fuzzy inference system, Response surface methodology-Genetic algorithm, and Non sorting genetic algorithm. The analysis takes into account engine load, blend percentage, nano-additive concentration, and injection pressure, and the desired responses are the thermal efficiency and specific energy consumption of the brakes, as well as the concentrations of carbon monoxide, unburned hydrocarbon, and oxides of nitrogen. Root-mean-square error and the coefficient of determination were used to assess the predictive power of the model. Comparatively to Artificial Intelligence and the Response Surface Methodology-Genetic Algorithm model, the results provided by NSGA-II are superior. This is because it achieved a pareto optimum front of 24.45 kW, 2.76, 159.54 ppm, 4.68 ppm, and 0.020243% for Brake Thermal Efficiency, Brake Specific Energy Consumption, Oxides of nitrogen, Unburnt Hydro Carbon, and Carbon monoxide. Combining the precision of ANFIS's prediction with the efficiency of NSGA-optimization II's gives a reliable and thorough evaluation of the engine's settings. The qualitative assessment considered practical aspects and engineering constraints, ensuring the feasibility of applying the parameters in real-world engine applications.

show abstract

“…Supply Chain disruption is defined as “An indicator of an organization's failure towards balancing the supply and demand sides,” [ 29 ]. Natural and man-made catastrophes, which occur infrequently but have a large impact on the SC [ [30] , [31] , [32] ], are the most common causes. The SC disturbances are expensive and possibly detrimental in the extended time [ 14 ] there has been a boom in publications in the last decade, and there is a vast amount of information on the issue.…”

Section: Identification Of Problems Related To Supply Chainmentioning

confidence: 99%

Assessment of challenges and problems in supply chain among retailers during COVID-19 epidemic through AHP-TOPSIS hybrid MCDM technique

Islam¹,

Khan

Ahmad³

et al. 2022

Internet of Things and Cyber-Physical Systems

Self Cite

View full text Add to dashboard Cite

Analytical research of artificial intelligent models for machining industry under varying environmental strategies: An industry 4.0 approach

Cited by 24 publications

References 20 publications

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS–GA–HSA Approach

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS–GA–HSA Approach

Modelling of compression ignition engine by soft computing techniques (ANFIS-NSGA-II and RSM) to enhance the performance characteristics for leachate blends with nano-additives

Assessment of challenges and problems in supply chain among retailers during COVID-19 epidemic through AHP-TOPSIS hybrid MCDM technique

Contact Info

Product

Resources

About