Volume 20, Issue 1

Cogeneration systems are widely used in industrial settings to meet process steam and power demands efficiently. Optimizing the load allocation among boilers and turbines is critical for improving overall system efficiency and reducing fuel consumption. However, traditional optimization approaches often rely on complex mathematical models, which can be challenging for in-house engineers without advanced optimization expertise. This study addresses this gap by utilizing a graph-theoretic tool, Process Graph (P-Graph), to optimize load allocation while considering the nonlinear part-load efficiency of boilers. Additionally, drum boilers exhibit nonlinear behaviors, such as the shrink-and-swell effect, which require advanced control strategies. To address this, various control strategies, including Proportional-Integral (PI) and Model Predictive Control (MPC), are evaluated under different high-pressure steam (HPS), medium-pressure steam (MPS), and energy demand scenarios. The study comprises two stages: optimization of load allocation among five boilers and three turbines, followed by the application of control strategies to the optimal configuration. Results show that dynamic load distribution involving Boiler 2, Boiler 3, Boiler 5, ST1, and CT achieves the best energy efficiency and cost-effectiveness. The comparison between the base and optimized scenarios underscores the changes in operating strategies achieved through optimization, resulting in a 2.38 % reduction in operating costs, equivalent to RM1727.3 per hour. Moreover, MPC outperforms PI control in closed-loop performance, demonstrating superior energy savings and error minimization.

In this paper, we introduce a numerical model designed to analyze the evaporation process of R134a refrigerant within a cold water production system. Using the ANSYS Fluent CFD software, our simulation solves the Navier–Stokes equation, thus offering insights into the system’s flow dynamics. Our investigation underscores the efficacy of CFD as a reliable tool for modeling and forecasting flow behaviors within complex systems. Specifically, we delved into the impact of varying the empirical coefficient in the Lee model. Our results indicate that augmenting the coefficient value amplifies the R134a flow rate at the evaporator outlet. Notably, a coefficient value of 0.25 closely matches the experimental flow rate. Furthermore, we analyzed the ascending velocity of R134a vapor, achieving a robust agreement between our numerical simulations and experimental observations. This study underscores the potential of numerical modeling in enhancing our understanding and prediction of refrigerant evaporation dynamics within cooling systems.

Hydrogen (H₂) and nitrogen (N₂) are both critical components in gasification processes, making efficient conversion of carbonaceous feedstocks into valuable gases with reduced environmental impact indispensable. This study demonstrates a state-of-the-art approach to predictive modeling for these quantities at high accuracy while allowing cost-effective solutions under a variety of operational conditions, enhancing safety, and enabling data-driven optimization. This work develops a new framework that incorporates the radial basis function model with two state-of-the-art optimization algorithms, namely the Zebra Optimization Algorithm (ZOA) and Flow Direction Algorithm (FDA), to enhance the predictive accuracy of gasification processes. This is a new frontier in optimizing the sustainable conversion of carbonaceous feedstocks, demonstrating the potential of data-driven methodologies in process efficiency and environmental sustainability. The RBFD model resulted in outstanding anticipation capability for H 2 , reaching an exceptional R 2 value of 0.997 during the whole period of testing. On the other hand, the RBZO framework proved to be the strongest predictor for N 2 anticipation, presenting an outstanding R 2 of 0.994 during the testing and validation phases. The RBFD and RBZO frameworks showed significantly higher productivity compared to the conventional RBF model, as evidenced by accuracy metrics like MSE, RMSE, and WAPE.

This paper applies an existing advanced model to improve key outputs in the continuous catalytic reforming (CCR) process for Persian Gulf Star Oil Company. Using tools like Aspen Custom Modeler and Aspen Plus, we focus on optimizing two main results: Research Octane Number (RON) and yield. A design of experiments was conducted to examine the effects of key input variables, including reactor temperatures and the hydrogen-to-hydrocarbon (H₂/HC) ratio, through 256 simulations. Various data fitting methods, including Response Surface Methodology (RSM), Radial Basis Function Network (RNLOOCV), and Artificial Neural Networks (ANN), were applied to describe process behavior. The Akaike Information Criterion (AIC)-optimized ANN model demonstrated the best performance, offering a balanced approach between accuracy and complexity. A sensitivity analysis revealed that increasing reactor temperatures improves RON but reduces yield due to enhanced cracking reactions. The H₂/HC ratio had a minimal impact on RON and yield, primarily serving to limit catalyst coke formation. Optimization using a genetic algorithm confirmed that optimal RON and yield can be achieved within specific temperature ranges. The results provide insights for enhancing CCR efficiency and refinery profitability.

The 4-hydroxyflavanes are derived from flavanones through a condensation reaction with a primary amine; therefore, the 4-hydroxyflavanes derivatives have been shown to have key biological actions such as aldose reductase inhibition, cancer chemoprevention, antibacterial and anti-inflammatory effects. In this study, we investigated the molecular docking interactions between 2-phenyl-4 (phenylamino)chroman-4-ol, 4-((3-chlorophenyl) amino)-2-phenylchroman-4-ol and 4-((3-methoxyphenyl) amino)-2-phenylchroman-4-ol diastereomers with methionine aminopeptidase II (MetAP2) which is an enzyme that is essential for tissue healing and cancer progression in humans, in order to find novel antiangiogenic agents. The molecular docking program used in this work was Moe software. The binding affinity composed of (MetAP2) with 4-((3-methoxyphenyl) amino)-2-phenylchroman-4-ol diastereomers derivatives (RR, RS, SR, SS) were found to be the most stable, with the free energy of −7.06, −7.85, −7.44, and −7.82 kcal/mol docking score and the RMSD 1.77, 1.24, 1.93 and 1.41 respectively; specifically, the RS configuration. The findings provide insights into the potential efficacy of 4-hydroxyflavanes derivatives as a (MetAP2) inhibitor, which could have implications for the development of novel cancer therapeutics.

Modelling the dynamics of cryogenic distillation columns is challenging due to their complex, nonlinear behaviour. This study introduces a novel identification approach using a hybrid Artificial Neural Network (ANN) optimized with Particle Swarm Optimization (PSO), applied to cryogenic distillation as a case study. The NARX-PSO-ANN model effectively captures the nonlinear dynamics of the distillation process by optimizing model parameters and avoiding local optima. The novelty of this work lies in integrating the NARX (Nonlinear Autoregressive with Exogenous Inputs) architecture with PSO, which enhances robustness and performance. To validate the model’s efficacy, realistic simulations of the cryogenic distillation column were conducted using Aspen Plus Dynamics, generated 2,000 data samples-1,400 training and 600 for validation. The NARX-PSO-ANN model was evaluated against established methods like BP-ANN and NARX-based BP-ANN, consistently outperforming them in identifying cryogenic distillation column dynamics and demonstrating superior effectiveness for complex separation processes. A user-friendly Python-based graphical user interface (GUI) was developed for real-time methane composition prediction, making the model accessible for practical applications. This innovative approach offers a reliable solution for optimizing complex, nonlinear systems in the process industry.

The integration of hydrogen as an energy carrier into an existing natural gas pipeline infrastructure presents a promising option for the decarbonization of the energy sector. However, using hydrogen in natural gas pipelines requires overcoming engineering challenges attributed to physical property and design considerations for both natural gas and hydrogen which may result in pipeline failures. To this end, this study employs a multi-software engineering approach to model both steady-state and transient behaviors of natural gas pipelines blended with hydrogen under cyclic variations. For transient state modeling, the Crank-Nicolson implicit scheme is applied to a 100 km pipeline with 200 nodes, using a time interval of 20 s in MATLAB. The one dimensional flow modeling aspects are analyzed in more detail with the finite element method in COMSOL. The study also highlights the importance of pipeline inventory analysis, focusing on the trade-off between gas transport capacity and flexibility; particularly with respect to packing and drafting operations. The intrinsic properties of hydrogen blends (0–20 %) are analyzed using the GERG Equation of State (EoS) in ASPEN. Results indicate that the COMSOL model shows less stability than the Crank-Nicolson method for abrupt demand changes, with a percentage error of ±11 % compared to the benchmark study. Additionally, inventory analysis suggests that to prevent pipeline failure, the pipeline can only be drafted for 3.6 h. Steady-state analysis reveals that blending hydrogen into natural gas significantly impacts pipeline design, with changes in specific gravity, compressibility factor, and speed of sound. The study also finds that higher hydrogen blending reduces the pressure drop under isothermal conditions, resulting in lower compressor power requirements. This modeling approach provides a foundation for strategies aimed at adapting natural gas infrastructure for hydrogen use.

This study carried out the simulation and optimization of a Venturi tube with the aim of producing more micro-nanobubbles (MNBs) and preventing their aggregation to increase mass transfer. In the first step, fluid flow in a steady state in a simple Venturi tube was simulated. In the next step, a tube will be added to the throat. The test design will investigate and optimize the effects of three geometrical parameters: length, diameter, and rotation angle of the tube on two responses pressure and velocity in the throat. Also, from the design of the experiment, it was found that the angle of rotation and the diameter of the tube, compared to the length of the tube, have a greater effect on increasing the velocity and reducing the pressure in the throat, and their values ​​were 90°, 1.5 mm, and 5 mm, respectively. From the simulation of the Venturi tube in the second state with the optimal values ​​obtained, a 51 % reduction in the cavitation number was achieved, which has an inverse ratio with the cavitation intensity. In general, with the increase in cavitation intensity, the production of MNBs increases, and their accumulation is minimized.