Carbon dioxide (CO₂) is the single largest contributor to climate change due to its increased emissions since global industrialization began. Carbon Capture, Storage, and Utilization (CCSU) is regarded as a promising strategy to mitigate climate change, reducing the atmospheric concentration of CO₂ from power and industrial activities. Post-combustion carbon capture (PCC) is necessary to implement CCSU into existing facilities without changing the combustion block. In this study, the recent research on various PCC technologies is discussed, along with the membrane technology for PCC, emphasizing the different types of membranes and their gas separation performances. Additionally, an overall comparison of membrane separation technology with respect to other PCC methods is implemented based on six different key parameters—CO₂ purity and recovery, technological maturity, scalability, environmental concerns, and capital and operational expenditures. In general, membrane separation is found to be the most competitive technique in conventional absorption as long as the highly-performed membrane materials and the technology itself reach the full commercialization stage. Recent updates on the main characteristics of different flue gas streams and the Technology Readiness Levels (TRL) of each PCC technology are also provided with a brief discussion of their latest progresses.

Solar drying is an environmentally friendly and sustainable approach to preserve agricultural products, particularly in regions with abundant sunlight. In terms of apricot cultivation, the implementation of solar drying methods can significantly impact post-harvest handling and storage. In the conditions of Uzbekistan, the drying of apricot products in indirect solar dryers is the next stage of development. This research aims to investigate the effectiveness of solar drying techniques for apricots in Uzbekistan, focusing on optimizing drying parameters and assessing the quality of the dried products. This study involves the design and implementation of solar drying systems for apricots. It primarily focuses on comparing and evaluating the drying times of apricots, changes in product moisture, and alterations in apricot color during the drying process using an Open Sun Dryer (OSD) and an Indirect Natural Convection Solar Dryer (INCSD). Various drying parameters such as the temperature, humidity, and drying time are monitored and controlled during the experiment. Additionally, the quality of the dried apricots is evaluated through analyses of their color, texture, and nutrient retention. According to the results, when apricots were dried in a solar dryer at 55 °C and with 35% humidity outside, their moisture content decreased from 85–90% to 12–18% within 15 h. The solar radiation intensity and ambient temperature play a crucial role in the acceleration of the time in the drying process. In general, utilizing equipment-based solar drying methods for apricots in Uzbekistan has the potential to rival traditional drying systems in terms of both quality and drying duration.

Industrial wastewater treatment processes often exhibit highly nonlinear, dynamic behavior, making accurate prediction and control difficult when using conventional modeling approaches. This study presents an enhanced Adaptive Neuro-Fuzzy Inference System (ANFIS) framework for modeling the ion-exchange purification process based on 200 experimentally collected data samples obtained from a laboratory-scale treatment system. The initial ANFIS structure was generated using subtractive clustering to automatically derive the rule base, while hybrid learning combining backpropagation and least-squares estimation was applied to train the model. The training results demonstrated stable convergence across 100, 200, and 300 epochs, with progressive improvements in model accuracy. To further enhance performance, several meta-heuristic optimization methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the Adam optimizer, were integrated within a Python 3.13-based environment to refine model parameters. Ensemble learning and an extended Boosting++ strategy was subsequently employed to reduce variance, correct residual errors, and strengthen generalization capability. The optimized ANFIS model achieved strong predictive accuracy across both training and unseen test datasets. The performance metrics for the full dataset yielded RMSE (Root Mean Square Error) = 1.3369, MAE (Mean Absolute Error) = 0.9989, and R² = 0.9313, while correlation analysis showed consistently high R-values for training (0.96745), validation (0.95206), test (0.95754), and overall data (0.96507). The results demonstrate that the combination of subtractive clustering, hybrid learning, meta-heuristic optimization, and ensemble boosting produces a highly reliable soft-computing model capable of effectively capturing the nonlinear dynamics of ion-exchange wastewater treatment. The proposed approach provides a robust foundation for intelligent monitoring and control strategies in industrial purification systems.

The drying process in indirect solar dryers is strongly influenced by rapidly changing ambient conditions, resulting in highly nonlinear and dynamic system behavior. Accurate modeling is therefore essential for performance evaluation, process optimization, and reliable prediction of the drying chamber temperature, which plays a key role in ensuring efficient moisture removal while preserving the nutritional and sensory quality of dried products. In this study, a fuzzy logic–based modeling approach using the Mamdani inference system is developed to predict the drying chamber temperature over a wide range of operating conditions. Experimental measurements were carried out with solar radiation varying from 400 to 950 W/m² and ambient temperature ranging from 20 to 50 °C, covering both static and dynamic system responses. The fuzzy model employs solar radiation and ambient temperature as input variables, represented by five and three triangular membership functions, respectively, while the drying chamber temperature is defined as the output variable using five triangular membership functions (T1–T5). The Mamdani inference system consists of 15 “if–then” rules, and centroid defuzzification is applied to obtain crisp output values. Model validation across the investigated operating range demonstrates a strong agreement between predicted and experimental temperatures. For example, at a solar radiation of 700 W/m² and an ambient temperature of 46 °C, the predicted chamber temperature is 50.9 °C compared to a measured value of 51.0 °C, while at 750 W/m² and 50 °C, the predicted temperature of 52.0 °C closely matches the experimental value of 51.8 °C. Statistical evaluation yields RMSE = 0.38 °C, MAE = 0.29 °C, and R² = 0.997, demonstrating effective temperature tracking capability within the tested operating range. These results show that the Mamdani fuzzy logic approach can effectively represent the thermal behavior of an indirect solar dryer within the tested operating range. The proposed model also provides a promising basis for the future development of real-time intelligent control strategies aimed at improving energy efficiency and product quality.

This study evaluates the impact of four drying methods—open sun drying, solar drying, infrared drying, and microwave drying—on the quality attributes and elemental retention of apricots (Prunus armeniaca L.). Experimental trials were conducted in June 2024 at the Tashkent Institute of Chemical-Technology using equal quantities of fresh apricots. Drying was continued until the moisture content, measured gravimetrically, dropped below 20% (wet basis), followed by spectroscopic analysis to determine macro- and microelement concentrations. Solar-dried apricots showed higher retention of essential nutrients in this experimental trial: potassium (2.37%), silicon (0.538%), magnesium (0.145%), calcium (0.176%), and sulfur (0.152%). In contrast, open sun drying led to significant nutrient degradation and poor visual quality. Microwave drying preserved some micronutrients but resulted in surface scorching due to uneven heating. Infrared drying yielded acceptable results but required substantial energy input. Among all methods, solar drying provided the optimal balance of high product quality and energy efficiency. The drying process required negligible electrical energy owing to exclusive reliance on solar radiation. This method supports sustainable food processing by reducing energy demand and greenhouse gas emissions while preserving nutritional quality. The results highlight solar drying as a promising, eco-friendly technique for preserving the nutritional integrity of agricultural products. These findings offer valuable scientific guidance for selecting appropriate drying technologies in the food processing industry, especially in regions with high solar potential. However, the study is limited to a single fruit variety and seasonal conditions.

This study presents an artificial neural network (ANN)-based predictive model for evaluating the drying efficiency of a cabinet-type solar dryer used for dehydrating Plantago major leaves under natural climatic conditions. The performance of solar drying systems is strongly affected by nonlinear and time-varying factors such as solar irradiance, drying-chamber temperature, and ambient relative humidity, which limits the accuracy of conventional modeling approaches. To address this challenge, a multilayer feedforward ANN was developed using solar irradiance, chamber temperature, and relative humidity as input variables and drying efficiency as the output. Experimental data comprising 120 samples were collected during summer conditions and divided into training, validation, and testing subsets. The ANN was trained using the Levenberg–Marquardt algorithm and demonstrated strong predictive performance, achieving an overall correlation coefficient of R = 0.9556 and a low mean squared error of $1.22\times {10}^{-4}$ The results confirm that the proposed ANN model can reliably capture the nonlinear drying behavior and accurately predict drying efficiency, providing a practical tool for real-time performance evaluation and supporting the development of intelligent monitoring and control strategies for cabinet-type solar drying systems.