Jaloliddin Eshbobaev

Early-stage estimation of total biogas yield is useful for preliminary screening of anaerobic digestion substrates when detailed kinetic parameters, long-term reactor data and substrate-specific biochemical methane potential tests are not yet available. This study develops a compact Gradient Boosting Regression model for estimating total biogas yield from basic substrate and operating descriptors commonly reported in anaerobic digestion studies. A literature-informed reconstructed benchmark dataset containing 120 records from twelve organic substrate groups was used. The response variable was total biogas yield, expressed as mL g⁻¹ volatile solids (VS) added; methane yield and methane content were not used as interchangeable target variables. Seven input variables were considered: volatile solids content, total solids content, carbon-to-nitrogen ratio, residence time descriptor, digestion temperature, pH and organic loading rate. The model was evaluated using the coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), five-fold cross-validation and residual diagnostics. On the test subset, the model achieved R² = 0.9885, MAE = 13.40 mL g⁻¹ VS, RMSE = 16.16 mL g⁻¹ VS and MAPE = 3.73%. The mean five-fold cross-validation score was R² = 0.9720 ± 0.0180, indicating stable interpolation performance within the investigated data domain. Feature-importance analysis identified C/N ratio as the dominant predictor, followed by volatile solids and total solids. However, residual diagnostics showed heteroscedasticity, and the low apparent importance of pH and temperature was attributed to their narrow ranges in the compiled dataset, rather than to biological irrelevance. The proposed model is therefore intended as an early-stage screening tool for total biogas yield estimation, not as a universal methane yield or design-level prediction model.

Higher education institutions are increasingly expected to reduce greenhouse gas emissions while maintaining reliable educational, laboratory, and administrative operations. This challenge is particularly relevant in transition economies, where university campuses often depend on fossil-fuel-based electricity systems, natural-gas heating, and aging infrastructure. This study presents a campus-scale decarbonization assessment for the Tashkent Institute of Chemical Technology in Uzbekistan. The quantified inventory covered Scope 1 emissions from natural-gas combustion and Scope 2 emissions from purchased electricity. Paper use, digital services, behavioural measures, and campus greening were assessed as supplementary institutional indicators and were excluded from the quantified total because consistent pre- and post-intervention activity data were unavailable. The assessment combined institutional utility records for 2023–2025 with information on renewable-energy deployment, heating modernization, digital transformation, sustainability awareness, and campus greening. A 300 kW solar photovoltaic system comprising 666 modules was commissioned in May 2023, with a documented annualized generation potential of approximately 520,000 kWh. Purchased grid electricity amounted to 711,402, 745,947, and 749,060 kWh in 2023, 2024, and 2025, respectively, while annual natural-gas consumption was 144,775, 161,200, and 142,031 m³. Using a conservative standard-based net calorific value of 31.8 MJ/m³ together with IPCC stationary-combustion factors, annual Scope 1 and Scope 2 emissions were estimated at 637.53, 685.29, and 652.65 tCO₂-eq, respectively. The 2025 total was 4.76% below the 2024 value but 2.37% above the 2023 value. The annualized PV technical potential corresponds to a theoretical maximum Scope 2 displacement of 276.64 tCO₂-eq/year under 100% self-consumption. This value does not represent measured generation or a realized emission reduction and was not included in the quantified inventory. Digitalization, behavioural engagement, and greening were evaluated as complementary measures rather than assigned separate emission-reduction credits. The study provides a transparent and regionally relevant framework for universities in transition economies seeking to strengthen campus carbon management under incomplete data conditions.

Post-combustion amine absorption is the most mature CO₂ capture technology, while membrane separation is a promising alternative due to its modularity and operational simplicity. Process intensification through exhaust gas recirculation (EGR) has also gained attention for increasing flue gas CO₂ concentration and reducing capture energy demand. However, comprehensive techno-economic comparisons of absorption, membrane, and hybrid systems under EGR-integrated NGCC conditions remain limited. This study presents a techno-economic assessment of multiple CO₂ capture configurations for a 450 MW NGCC power plant. Process simulations were performed using Aspen Plus and Aspen Custom Modeler to evaluate absorption, membrane, and hybrid systems integrated with conventional and selective EGR. The analysis considers full steam cycle integration and compares energy consumption, net plant efficiency, levelized cost of electricity (LCOE), and CO₂ avoidance cost. The results show that EGR significantly improves carbon capture performance. Among all cases, selective EGR combined with amine absorption delivers the best performance, reducing the energy penalty by more than 30% compared with standalone absorption and by over 70% relative to membrane separation. This configuration achieves an LCOE of approximately 72 USD/MWh and a CO₂ avoidance cost of about 39 USD/tCO₂, outperforming the selective EGR–membrane system (77 USD/MWh and 51 USD/tCO₂). These findings demonstrate that integrating selective EGR with amine absorption is a highly promising strategy for improving the technical and economic feasibility of CCSU in NGCC power plants.

This study presents a comparative evaluation of solar cabinet drying and traditional open-air sun drying with respect to their influence on the retention of water-soluble vitamins and flavonoids in Plantago major L. leaves, aiming to identify an effective and sustainable drying strategy for functional food applications. Freshly harvested leaves were subjected to both drying methods under comparable environmental conditions. To account for possible structural heterogeneity, external and internal leaf tissues were analyzed separately. Qualitative and quantitative determination of bioactive compounds was performed using high-performance liquid chromatography with diode-array detection (HPLC-DAD). Flavonoids were analyzed at detection wavelengths of 254 and 276 nm, while water-soluble vitamins (C, B₂, B₃, B₆, and B₉) were determined at 250 nm. Quantification was carried out using external calibration, and results were expressed as concentrations (mg/g dry matter). The results demonstrate that solar cabinet drying provides superior preservation of oxidation- and light-sensitive bioactive compounds compared to open-air sun drying. In particular, vitamin C content in solar cabinet-dried samples reached 91.62 mg/g, which was more than three times higher than that observed after open-air drying (26.90 mg/g). Solar cabinet drying also enhanced the retention of key antioxidant flavonoids, notably dihydroquercetin (14.23 mg/g vs. 11.21 mg/g) and luteolin (0.38 mg/g vs. 0.26 mg/g). Although slightly higher concentrations of certain compounds, such as rutin and vitamins B₆ and B₉, were detected in open-air-dried samples, the overall nutraceutical profile favored solar cabinet drying. In conclusion, the controlled microclimate of the solar cabinet dryer significantly improves the stability and retention of critical water-soluble vitamins and antioxidant flavonoids in Plantago major L. leaves. These findings confirm that solar cabinet drying is a nutritionally advantageous, energy-efficient, and sustainable approach for producing high-quality plant-based ingredients suitable for functional food and nutraceutical applications.

This paper presents a mathematical modeling and advanced control strategy for the beer fermentation process, which is characterized by nonlinear biochemical kinetics and time-dependent dynamics. A biokinetic model was developed to describe the relationship between yeast growth, sugar consumption, and ethanol formation. The system was represented as a cascade of several continuous stirred-tank reactors (CSTRs), and experimental data confirmed a fermentation cycle of approximately 10 days. During this period, biomass concentration reached 6.8 g/L and ethanol levels exceeded 42 mmol/L. Substrate concentration (S) declined from 120 to 5 g/L, demonstrating effective conversion. The model was linearized around an operating point and reformulated into a 12-state-space system with input variables: temperature (set at 20–22 °C) and pH (maintained within 4.2–4.5). These inputs were controlled using fuzzy logic control (FLC) and model predictive control (MPC). Simulation results indicated that the FLC reduced temperature deviation to ±0.3 °C and minimized pH fluctuation below ±0.05. The MPC strategy improved substrate consumption efficiency by 8.5% and decreased fermentation time by 12 h under optimized input profiles. The combined FLC–MPC scheme demonstrated superior robustness, smooth trajectory tracking, and adaptability to biological variability compared to traditional methods. The developed framework supports intelligent brewery automation and provides a scalable foundation for further integration of digital fermentation technologies.

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.

Free chlorine removal from industrial wastewater using activated carbon filtration requires accurate modeling and optimal control to balance treatment efficiency and adsorbent consumption. In this study, a combined experimental–machine learning–optimization framework was developed to predict and optimize residual chlorine concentration in a pilot-scale activated carbon filtration unit. A total of 200 experimental runs were collected using a pilot activated carbon filtration system by varying flow rate, initial chlorine concentration, pressure, pH, temperature, and carbon dose. Two ensemble learning models, Random Forest (RF) and Gradient Boosting (GB), were trained and validated using five-fold cross-validation. Both models exhibited high predictive accuracy, with GB outperforming RF on the full dataset (R² = 0.9995, Root Mean Square Error (RMSE) = 0.0355 mg·L⁻¹, Mean Absolute Error (MAE) = 0.0276 mg·L⁻¹) and on the independent test set (R² = 0.9417). Feature importance and partial dependence analyses revealed that the initial chlorine concentration and activated carbon dose were the dominant controlling variables, while increasing flow rate led to higher residual chlorine levels. A multi-objective optimization strategy based on Pareto dominance was implemented using the trained GB model as a surrogate to simultaneously minimize residual chlorine and carbon consumption. The optimal compromise solution corresponded to an activated carbon dose of approximately 51.5 kg and a residual chlorine concentration of 0.156 mg·L⁻¹ at a flow rate of 43.1 m³·h⁻¹. The proposed framework demonstrates a reliable and cost-effective approach for predictive control and sustainable optimization of dechlorination processes in industrial wastewater treatment.

Solar drying is a sustainable and energy-efficient method for preserving agricultural products; however, its performance is strongly influenced by fluctuating environmental conditions. This study presents an artificial neural network (ANN)-based predictive temperature control system for an indirect forced-convection solar dryer. A data-driven dynamic model of the drying process was developed using experimental measurements and implemented in MATLAB R2014a (MathWorks, Natick, MA, USA). The proposed ANN-based controller was evaluated against a conventional PID controller under identical operating conditions. The results show that the ANN-based approach reduced the settling time by approximately 36% (160 s compared to 250 s for PID) and maintained drying chamber temperature stability within ±1.2 °C. These improvements demonstrate the effectiveness of neural predictive control for enhancing dynamic response and temperature regulation accuracy in solar drying systems. The study is limited to a prototype-scale dryer and short-term experimental data; therefore, further validation under varying climatic conditions and larger-scale systems is required.

Post-combustion CO₂ capture using monoethanolamine (MEA) is a mature mitigation technology, yet its high energy demand and complex dynamics remain major challenges. This study presents a unified dynamic modeling and control framework for an MEA-based absorption–regeneration system, focusing on a comparative evaluation of PID, fuzzy logic control (FLC), and model predictive control (MPC) under realistic operating disturbances. A control-oriented surrogate model was developed in MATLAB R2024b/Simulink and validated against published benchmark trends. The control objective was to maintain CO₂ capture efficiency above 90% while minimizing reboiler energy consumption under ±10% inlet CO₂ concentration and flue gas flow disturbances. Simulation results showed that PID control ensures basic stability but exhibits slow recovery and high energy usage, while FLC improves robustness with limited dynamic improvement. MPC consistently maintained capture efficiency above the target value, reduced the settling time by approximately 37%, and achieved a 12.4% reduction in average reboiler duty compared to PID control. The results demonstrate that a unified, implementation-oriented modeling framework enables the effective assessment of advanced control strategies and supports the energy-efficient operation of industrial MEA-based CO₂ capture systems.

Ion-exchange-based wastewater treatment processes exhibit nonlinear and time-varying dynamics, making the control of total dissolved solids (TDS) and water hardness a complex task. Conventional Proportional–Integral–Derivative (PID) controllers often show limited performance under such conditions due to fixed tuning parameters and linear assumptions. To address these limitations, this study presents a comparative evaluation of traditional and intelligent control strategies for regulating TDS and water hardness through influent flow control. A classical PID controller is compared with fuzzy logic and Adaptive neuro-fuzzy inference system (ANFIS) controllers using a unified MATLAB/Simulink simulation framework. The control performance is evaluated based on dynamic response characteristics, including rise time, settling time, and overshoot. For TDS control, the PID controller exhibits a rise time of 15.9 s and a settling time of 50.9 s, while the fuzzy logic controller improves the response with a rise time of 13.6 s and settling time of 44.1 s. The ANFIS controller achieves the fastest response, with a rise time of 8.31 s and a settling time of 27.1 s. Similar trends are observed for water hardness control, where the PID controller shows a rise time of 17.0 s and settling time of 55.8 s, the fuzzy logic controller reduces these values to 12.3 s and 40.4 s, respectively, and the ANFIS controller further improves performance with a rise time of 9.23 s and settling time of 30.3 s. The overshoot values for all controllers remain comparable, within the range of approximately 4.4–5.0%. The results clearly demonstrate that intelligent control strategies, particularly ANFIS, provide significantly faster convergence and improved dynamic performance compared to conventional PID control. The reduced settling time implies lower control effort and decreased energy consumption, highlighting the potential of intelligent controllers for efficient and reliable industrial wastewater treatment applications.