Journal of Engineering

Many features of nanofluids, such as the Prandtl number and viscosity, are researched as the number of studies conducted in the field of nanofluids increases. Observations on the Prandtl number and viscosity of titanium oxide nanofluids are made in this study. These observations are made at low concentrations of titanium oxide nanoparticles and temperatures ranging from 30.4°C to 70.4°C. Novel correlations for viscosity and Prandtl number as functions of temperature have been developed and compared to the previously published models for Prandtl number and viscosity. The results indicate that titanium oxide-ethylene glycol nanofluid has a greater viscosity and Prandtl number than all other titanium oxide nanofluids observed in the study at 0.01 nanoparticle concentration. The results on viscosity and Prandtl number for the new correlations fall within the same range as those found in the literature, indicating that the new correlations introduced as functions of temperature in this study can be used in future research to establish viscosity and Prandtl number calculations for the different types of nanofluids at specific temperatures.

It has been demonstrated that B20 biodiesel can be used in diesel engines without modifying their specifications. It is still being developed to reduce dependence on diesel fuel by developing biodiesel with a higher percentage of palm oil. Alcohol is added to biodiesel to reduce problems with fuel injection due to its high viscosity. In this study, the biodiesel properties of diesel-palm cooking oil-methanol/ethanol/butanol blends are investigated in relation to diesel engine performance and potential exhaust emissions. The percentages of palm cooking oil used were 30%, 40%, 50%, 60%, 70%, 80%, and 85%. A 15% concentration of methanol, ethanol, and butanol alcohol was used. According to chemical and physical tests, biodiesel with a higher palm oil content had poorer properties. A biodiesel blend that combines diesel, palm oil, and methanol can perform almost as well as diesel fuel in terms of exhaust emissions and engine performance.

Due to the difficulties inherent in diagnostics and prognostics, maintaining machine health remains a substantial issue in industrial production. Current approaches rely substantially on human engagement, making them costly and unsustainable, especially in high-volume industrial complexes like fulfillment centers. The length of time that fulfillment center equipment failures last is particularly important because it affects operational costs dramatically. A machine learning approach for identifying long and short equipment failures is presented using historical equipment failure and fault data. Under a variety of hyperparameter configurations, we test and compare the outcomes of eight different machine learning classification algorithms, seven individual classifiers, and a stacked ensemble. The gradient boosting classifier (GBC) produces state-of-the-art results in this setting, with precision of 0.76, recall of 0.82, and false positive rate (FPR) of 0.002. This model has since been applied successfully to automate the detection of long- and short-term defects, which has improved equipment maintenance schedules and personnel allocation towards fulfillment operations. Since its launch, this system has contributed to saving over $500 million in fulfillment expenses. It has also resulted in a better understanding of the flaws that cause long-term failures, which is now being used to build more sophisticated failure prediction and risk-mitigation systems for fulfillment equipment.

In response to China’s “double carbon” strategic goal, the problem of large emissions of coal gangue and low utilization rate of gangue cemented backfill with fly ash as admixture needs to be solved urgently. Based on this, this paper uses coal gangue powder as admixture and coal gangue as coarse and fine aggregate to prepare coal-gangue-based green cemented filling body (CGGCFB) with different mass concentrations (MC). The workability, mechanical properties, and microscopic pore structure (nuclear magnetic resonance and scanning electron microscope) were measured, respectively, to explore the effect of MC on the above mentioned properties. In terms of macroscopic properties, with the increase of MC from 80% to 82% and 84%, the slump of filling slurry decreased by 18.37% and 32.65%, the expansion decreased by 30% and 54.55%, and the bleeding rate decreased by 28.3% and 60.38%. The uniaxial compressive strength (UCS) of CGGCFB at different ages increased to varying degrees, and the maximum was 2.9 times of the original. In terms of microscopic pore structure characteristics, the total number of pores of CGGCFB decreases, the pore size changes from large to small, the shape tends to be uniform and circular, the force characteristics of the pores are improved, the complexity and tortuousness of the pore channels increase, and the inner surface of the pores becomes rough; the test results show that the performance of the backfill with a mass concentration of 82% is reasonable, and the utilization rate of coal gangue can be increased to 72%. The research results of this paper can further improve the utilization rate of coal gangue resources, and provide a theoretical reference for the research and application of coal gangue in mine filling.

The shear process of small batch and multivariety copper strip processing enterprises are the bottleneck of production, which often faces the contradiction between working efficiency (less tool changing) and yield (reducing geometric waste). The high yield of copper strip embryo is the core index of high yield and high benefit for enterprise. Less tool changing is a key step for high efficiency and fast product delivery in shear station. In this paper, we took the cutting production of the production management system as a research example in Hubei Lean High Precision Copper Strip Company when the system is developing. We used the penalty function to deal with the length floating constraint. Then, we established a multiobjective optimization model with the roll weight and the number of tool changing as the weights, which were calculated by an integrated weighting method. Three algorithms, namely, adaptive particle swarm optimization, niche genetic algorithm based on crowding, and niche genetic algorithm based on seed retention (NGA), were used to solve the problem. Through production examples, it was concluded that the solution solved by NGA has the highest utilization rate of the coil when the number of tool changing was as little as possible. This paper provides a new solution combining the efficiency and benefit for shear process in finished product delivery of copper strip processing enterprises.

The article gives the performance and reliability analysis of renewable PV systems fed with a double boost converter. For analyzing the PV system performance and reliability, a 660 W PV system is considered and a double boost converter with the PSO MPPT method is implemented by adding a secondary path to the conventional boost converter, which shares the input current and reduces the current ripples in the system. For a detailed analysis of the system, different solar irradiations of 1000 W/m², variable irradiations, and real-time solar irradiation in the area of 13⁰39¹29.3¹¹N 79⁰29¹09.3¹¹E on May 2022 are considered and reliability indices are presented at PV system and power electronic converter components. A MATLAB/Simulink environment is chosen for analysis of the proposed system in different conditions.