The use of pyrolysis oil can be seen as an alternative fuel for maritime transport. However, pyrolysis oil from tires must be desulfurized for this. Recently, this can be done by hydrodynamic cavitation. This process does not require oxidation chemicals but only water, a cavitation generator, and a pump to drive it. In the literature, this concept has been successfully tested on model fuels. In this study, the cavitation generator for the desulfurization of waste tire pyrolysis oil was printed from polylactic acid-based on simulations of the optimal design, which allows for much cheaper production and easy replacement in case of wear or testing of alternative designs. After 60 min of treatment at 5 bar inlet pressure, a desulfurization of almost 33% was achieved. Furthermore, an interaction analysis showed that only from a pyrolysis oil content of 5.5 to 6% does hydrodynamic cavitation have an effective effect on desulfurization.

The development of electric vehicles (EVs) and their power source systems (PSS) is a rapidly growing field of technology. However, the EV's travel distance (range between charging stations) depends on the agility of the PSS, or battery capacity system. EV driving range and battery capacity are the two most significant technical challenges in commercializing EVs. This study aims to propose an integrated model that identifies the optimal energy factor orientation, enabling EVs to cover the maximum travel distance and reach the charging station for their next trip. Additionally, the artificial intelligence (AI) and statistical models were integrated and applied to predict, validate, and explain how energy factors affect the driving range of EVs. The developed models and validations revealed that maintaining precise assimilation of battery power factors can vary the EV's travel distance from 60 to 610 km. In this case, we have identified 77.5 kWh battery capacity and 14.5 kW charging capacity as the optimum power source factors. After 5.5 h of charging, various adjustments to power source factors allow for optimum battery performance. We have also proposed the central composite factorial design (CCFD) to compute the impact of energy factors on travel distance. The study used the response surface methodology (RSM) and an in-house-developed AI-based algorithm to achieve the research results. The alignment percentage between model-predicted data and real-time outputs showed an extremely high precision of over 95 % and confidence in the findings' reliability.

This paper investigates pouch-type lithium-ion battery cells with a nominal voltage of 3.7 V and a nominal capacity of 57 Ah. A numerical model of the cell was developed and implemented using the NTGK method, which accurately predicts electrochemical and thermal processes. The results of numerical modeling matched with the experimental results of battery cell temperature measurements—the average deviation was about 4.5%; therefore, it can be considered reliable for further engineering research and construction of battery modules. In the experimental part of the paper, the battery cell was loaded in various C-rates (from 0.5 to 2 C), using heat flux sensors, thermocouples, and a thermal imaging camera. The studies revealed that the highest temperature is in the tabs area of cells. The temperature on the face of the cell surface exceeds 35 °C already from a load of 1.35 C, which accelerates cell degradation and reduces the number of cycles. Thermal imaging revealed uneven temperature distribution, whereby the top of the cell heats up more than the bottom of the cell and the temperature gradient can reach 2–4 °C. It was observed that during faster charge/discharge modes, the temperature rises from the tabs of the cell, and during slower ones, more in the middle face surface of the cell. The studies highlight the need to apply additional cooling solutions, especially cooling of the upper cell face, to ensure durability and uniform heat distribution.

This study solved a set of equations to verify the dynamic optimal conditions of nonthermal plasma (NTP)-chemical conversion of solid polyolefin wastes into liquid petroleum hydrocarbons. Furthermore, a novel optimisation model was validated with non-linear experimental conditions to assess the quantitative relationship between the process variables responsible for the degradation rate of wastes. The central composite design (CCD) experimental design was developed based on the Response Surface Model (RSM) technique. These techniques significantly improved the model predictions because of the more-detailed electrochemical description. Experiments were conducted in an in-house-designed and -developed NTP system with advanced data acquisition schemes. Both experimental and the numerical findings exhibited a good agreement, and the results indicated that the electrical factors of NTP could significantly affect the conversion yield (Y_conv%) of solid polyolefin-derived wastes to liquid hydrocarbons. Additionally, the model investigation indicated that factors such as power discharge ($x_1$), voltage intensity ($x_2$), and reaction retention time (RTT) ($x_3$) significantly influenced the conversion yield. After optimisation, a maximum conversion percentage (Y_conv%) of ≈93% was achieved. The findings indicated that this recommended framework could be effectively employed for scaling the plasma synergistic pyrolysis technique for generating the maximal Y_conv% of plastic wastes to yield an oil. Thereafter, the analysis of variance (ANOVA) technique was applied to examine the accuracy of the developed structure in order to upgrade this laboratory-scale processes to an industrial-scale process with >95% effectiveness. The calorific value of the produced oil was seen to be from 43,570.5 J/g to 46,025.5 J/g due to changes of the arrangements of the process factors, which specified that the liquid hydrocarbons showed similar characteristics like commercial diesel in this respect.

The efficiency of employing a multifactorial approach to enhance the nonthermal plasma (NTP) chemical conversion of solid waste food packaging materials into liquid petroleum hydrocarbons was assessed for the first time in this study. The researchers adopted a hybrid approach which integrated the zero-dimensional (0-D) and response surface model (RSM) techniques. After their application, the researchers noted that these strategies significantly enhanced the model prediction owing to their accurate electrochemical description. Here, the researchers solved a set of equations to identify the optimisation dynamics. They also established experimental circumstances to determine the quantitative correlation among all process variables contributing to food plastic packaging waste degradation and the production of liquid fuels. The findings of the study indicate a good agreement between the numerical and experimental values. It was also noted that the electrical variables of NTP significantly influenced the conversion yield (Y_conv%) of solid plastic packaging waste to liquid hydrocarbons. Similarly, after analysing the data, it was seen that factors like the power discharge rate ($x_1$ ), discharge interval ($x_2$), power frequency ($x_3$), and power intensity ($x_4$) could significantly affect the product yield. After optimizing the variables, the researchers observed a maximal Y_conv% of approximately 86%. The findings revealed that the proposed framework could effectively scale up the plasma synergistic pyrolysis technology for obtaining the highest Y_conv% of solid packaging plastic wastes to produce an aromatics-enriched oil. The researchers subsequently employed the precision of the constructed framework to upgrade the laboratory-scale procedures to industrial-scale processes, which showed more than 95% efficiency. The extracted oil showed a calorific value of 43,570.5 J/g, indicating that the liquid hydrocarbons exhibited properties similar to commercial diesel.