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In this paper, environmental impact analysis is applied to the various auxiliary power units (APUs) used for commercial aircraft in air transportation sector. The exhaust emissions of different auxiliary power units used in commercial aircraft are investigated. The emission index ( EI ), global warming potential ( GWP ) rate, global warming potential index ( GWPI ), environmental impact ( EnI ) rate, environmental impact index ( EnII ), environmental damage cost ( EDC ) rate, and environmental damage cost index ( EDCI ) of the exhaust emissions of APUs are computed. The GTCP36-300 model APU has the lowest total emission rate (TER) with 1.333 kg/h, the GTC85-129 model APU has the maximum total environmental index (TEI) by 24.719 g/kg-fuel, the GTCP36-300 model APU has the best total global warming potential value with 2709.176 kg/h CO 2_eqv , the TSCP700 model APU has the worst global warming potential index rate as 52.481 kg/kWh CO 2_eqv , the best total environmental damage cost rate is calculated to be 3.717 €/h for GTC85-72 model APU, the TSCP700 model APU has the highest environmental damage cost index with 0.130 €/kWh, the maximum total environmental impact is computed to be 5656.378 mPts/h for GTCP660 model APU, and the best total environmental impact index is determined for the GTC85-72 model APU.

This paper introduces a complete real-time algorithm, where the chatter is detected and eliminated by spindle speed manipulation via the chatter energy feedback calculated from the axis encoder measurement. The proposed method does not require profound knowledge of the machining dynamics; instead, the entire algorithm exploits the fact that milling vibrations consist of forced vibrations at spindle speed harmonics and chatter vibrations that are close to one of the natural modes, with sidebands which are spread at the multiples of spindle speed frequency above and below the chatter frequency. The developed algorithm is able to identify the amplitude, phase and frequency of all the harmonics constituting the periodic forced and chatter vibrations. The key challenge is to select dominant chatter frequencies for the calculation of a robust and accurate chatter energy ratio feedback; this is achieved by utilizing the frequency estimation variance of EKF as a novel chatter indicator. Based on the chatter energy ratio feedback, the controller overrides the spindle speed in order to suppress the chatter energy below a particular threshold value. The varying spindle speed challenge is handled by updating the state transition matrices of the Kalman filters and real-time calculation of the band-pass filter coefficients, based on the derived discrete time transfer functions. The developed algorithm is tested on a Deckel FP5cc CNC which is in-house retrofitted and has a PC-based controller for the real-time application of the proposed algorithm. It is shown that the real-time chatter frequency and amplitude estimates are compatible with off-line FFT analysis, and chatter can be successfully eliminated by energy feedback.

Frequency response functions (FRFs) are one of the most useful methods for representing machine tool dynamics under force excitation. FRFs are usually obtained empirically through output measurements, and force excitations are controlled by an external device such as hammers or shakers. This study offers an operational identification method that utilizes the calculation of force applied during the process as an input for FRF identification. Force excitation is provided through the face milling of a thin-walled workpiece, and acceleration measurements are taken during the process. The FRF is calculated at a designated position by sampling workpiece-cutting tool contacts as individual tap tests and substituting a force calculation as input. Force coefficients need to be known for the force calculation. An experimental force coefficient identification method is proposed. In that case, a similar thin-walled workpiece at a point with known FRF and acceleration measurements is utilized. Results are confirmed with FRFs obtained in the same location for both FRF identification and force coefficient identification approaches.

In the present study, a novel combined power cycle for solar power tower (SPT) system consisting of helium Brayton cycle (HBC) and transcritical CO2 (TCO2) for waste heat recovery is being studied for carbon‐free generation. The performance of the proposed SPT based combined cycle (SPT‐HBC‐TCO2 cycle) is compared with SPT based basic cycle (SPT‐HBC) based on exergoeconomic and thermodynamic analyses. It is concluded that the SPT‐based combined cycle (SPT‐HBC‐TCO2 cycle) produces a thermal efficiency of 32.39% and exergy efficiency of 34.68% with an electricity cost of 1.613 UScent kWh−1. The exergy and thermal efficiency of the SPT‐based combined cycle are enhanced by 13.18% and 13.21% respectively, while electricity cost is reduced by around 2% as compared to the SPT‐based basic cycle (SPT‐HBC) configuration at base conditions. A notable finding is that, despite the additional expenditures related to the bottoming cycle, the cost of electricity is lesser for the proposed combined cycle. Additionally, a comparison with the related prior published research exhibits that the performance of the current novel system is superior to that of the systems based on steam rankine cycle and supercritical CO2 cycles. A novel combined power cycle for solar power tower (SPT) system consisting of helium Brayton cycle (HBC) and transcritical CO2 (TCO2) for waste heat recovery is studied for carbon‐free generation. The performance of the proposed SPT based combined cycle (SPT‐HBC‐TCO2 cycle) is compared with SPT based basic cycle (SPT‐HBC) based on exergoeconomic and thermodynamic analyses.

In this study, renewable and sustainable biofuel production from waste cooking oil and its blends with diesel fuel are investigated in terms of specific fuel properties. The fuel blends are named “Renewable Biofuel (RBF) 20” (20% biofuel–80% diesel), “Renewable Biofuel 50” (50% biofuel–50% diesel), and “Renewable Biofuel 100” (100% biofuel). The acid number, flash point, viscosity, cloud point, density, and pour point fuel properties of the new Renewable Biofuels are experimentally obtained and compared with diesel fuel. The viscosities of the biofuels are found to be 2.774 mm2/s for Renewable Biofuel 20, 3.091 mm2/s for Renewable Biofuel 50, and 4.540 mm2/s for Renewable Biofuel 100. Renewable Biofuel 20 has the minimum density value among biofuels. The density of Renewable Biofuel 20, Renewable Biofuel 50, and Renewable Biofuel 100 are obtained as 835 kg/m3, 846 kg/m3, and 884 kg/m3, respectively. More energy can be released with the use of Renewable Biofuel 100 in terms of heating value. The new fuel specification of biofuels can contribute to the fuel industry and help the studies on fuels for diesel engines.

In this study, thermodynamic analysis is implemented to the kerosene‐fuelled high by‐pass turbofan (HBP‐TF) engine to assess entropy, exergy, environmental, and sustainability metrics for different design variables such as pressure ratio of high‐pressure compressor (HPC‐PR) ranging from 7.5 to 8.5 and turbine inlet temperature (TIT) varying from 1400 to 1525 K considering variable needs in the aviation industry. As a novelty, entropic improvement potential (EIP) index for turbomachinery components and specific irreversibility production for the whole engine are calculated. Sustainability‐based parameters for different cases are compared with the baseline values of the HBP‐TF engine. The combustor has the highest entropy production of 44.4425 kW K−1 at the baseline. The higher TIT increases the entropy production of the combustor by 16.56%, whereas the higher HPC‐PR decreases it by 5.83%. The higher TIT and HPC‐PR favorably affect the sustainable efficiency factor of the engine, which is observed as 1.5482 at baseline and increases by 4.5% and 0.058% with the increment of TIT and HPC‐PR, respectively. The higher TIT and higher HPC‐PR results in lowering sustainability of the engine. The specific irreversibility production of the engine decreases by 3.78% and 0.1171% respectively, as TIT and HPC‐PR reach the highest point considered in the study. Thermodynamic analysis is applied to the kerosene fuelled high by‐pass turbofan (HBP‐TF) engine to assess entropy, exergy, environmental and sustainability metrics for different design variables. As a novelty, entropic improvement potential index for turbomachinery components and specific irreversibility production for the whole engine are computed. Sustainability‐based parameters for different cases are compared with the baseline values of HBP‐TF engine.

Porous geopolymer materials can be used in various fields such as thermal insulation, filtration, catalyst, and building materials. In this study, open porous geopolymer‐based cordierite materials are produced due to the porous structure, temperature resistance, and easy and low‐cost applications of geopolymer structures, which are oxide ceramic materials that can act as natural catalysts for emission treatment of diesel engines. For the composition of cordierite, waste boron clay, metakaolin, fly ash, and magnesium carbonate are used, while keeping the geopolymerization temperature constant, sodium silicate, sodium hydroxide, polypropylene, and glass fiber, hydrogen peroxide are used to create an alkaline environment. These materials are tested in a 4‐stroke 4‐cylinder diesel engine's exhaust system at 50, 75, and 100 Nm engine torques and 1500, 1700, and 1900 rpm engine speeds. The use of open‐cell geopolymer materials reduces CO emissions by 66%, NOx emissions by 25% and HC emissions by 68%. The open‐cell geopolymer materials are found to be effective in treating over 95% of particulate matter. The chemical and microstructures of the obtained open‐cell geopolymer structures are investigated. It is concluded that the developed products are useful tools for the emission treatments of diesel engines considering the oxidizing and filtering effects of the materials. Open porous geopolymer‐based cordierite materials are produced due to the porous structure, temperature resistance, easy and low cost applications of geopolymer. For the composition of cordierite, waste boron clay, metakaolin, fly ash and magnesium carbonate are used, while keeping the geopolymerization temperature constant, sodium silicate, sodium hydroxide, polypropylene and glass fiber, hydrogen peroxide are used to create an alkaline environment. These materials are tested in a diesel engine to show the effective emission treatment abilities of the developed products.

In this paper, the exergy analysis and environmental assessment are performed to the biodiesel and diesel-fueled engine at full 294 Nm and 1800 r/min. The exergy loss rates of fuels are found as 15.523 and 18.884 kW for the 100% biodiesel (BDF100) (obtained from cooking oil) and Japanese Industrial Standard Diesel No. 2 (JIS#2) fuels, respectively. In addition, the exergy destruction rate of the JIS#2 fuel is found as 80.670 kW, while the corresponding rate of the BDF100 is determined as 62.389 kW. According to environmental assessments of emissions and nanoparticles of the fuels, the biodiesel (BDF100) fuel is more environmentally benign than the diesel (JIS#2) fuel in terms of particle concentration and carbon monoxide and hydrocarbon emissions. So, it is better to use this kind of the 100% biodiesels in the diesel engines for better environment and efficiency in terms of the availability and environmental perspectives.

In this study, a Grey‐box (GB) model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger (STHE) under varying process conditions. Aspen Exchanger Design and Rating (Aspen‐EDR) was initially used to construct a first principle model (FP) of the STHE using industrial data. The Genetic Algorithm (GA) was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions. A dataset comprised of optimum process conditions was generated through FP‐GA integration and was utilised to develop an Artificial Neural Networks (ANN) model. Subsequently, the ANN model was merged with the FP model by substituting the GA, to form a GB model. The developed GB model, that is, ANN and FP integration, achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP model. Performance of the GB framework was also comparable to the FP‐GA approach but it significantly reduced the computation time required for estimating the optimum process conditions. The proposed GB‐based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.

Hardware‐based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes. A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of ±5% in process conditions, such as temperature, flow rates, etc. The generated data was used to train support vector machines (SVM), Gaussian process regression (GPR), artificial neural networks (ANN), regression trees (RT), and ensemble trees (ET). Hyperparameter tuning was performed to enhance the prediction capabilities of GPR, ANN, SVM, ET and RT models. Performance analysis of the models indicates that GPR, ANN, and SVM with R2 values of 0.99, 0.978, and 0.979 and RMSE values of 0.108, 0.262, and 0.258, respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables. ET and RT had an R2 value of 0.94 and 0.89, respectively. The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method. Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%. The proposed methodology of surrogate‐based optimisation will provide a platform for plant‐level implementation to realise the concept of industry 4.0 in the refinery.