Volume 19, Issue 4

The article discusses the profound impact of advancements in computing and software on theoretical simulations, marking a transformative era in computational chemistry. Focused on theoretical coordination chemistry, it delves into the historical context and underscores the contemporary importance of computational methods. Coordination materials, involving metal atoms surrounded by ligands, are highlighted for their pivotal roles across scientific disciplines. The manipulation of ligands and metal ions within these compounds offers diverse functionalities, from catalytic modifications to enhancing oxygen transport in biological systems. The comprehensive review explores the basics of coordination materials, detailing examples across various categories. Theoretical approaches, including quantum mechanics methods like density functional theory (DFT) and Monte Carlo simulations, are thoroughly examined. The article emphasizes crystallography techniques for Metal-Organic Frameworks (MOFs) and concludes by emphasizing the exponential growth in computing power, making modeling and simulation indispensable in molecular and material research. The development of an integrated computational strategy rooted in DFT is highlighted as a crucial advancement, bridging precision and computational practicality. This holistic approach advances understanding in coordination chemistry and nanostructured materials, paving the way for innovative applications and discoveries.

We investigate a model for an activated sludge process that contains a chemical reactor unit attached to a bioreactor. Inside the chemical reactor unit, a process takes place which increases the decay coefficient of heterotrophic biomass. This mimics a number of experimental techniques which are used to decrease the mass of sludge. Such techniques are of growing interest as the activated sludge process produces large volumes of sludge; the costs associated with its disposal are significant. Our primary interest is to investigate how the operation of the chemical reactor unit changes the steady-state concentrations of both the total suspended solids within the biological reactor and the chemical oxygen demand in the effluent stream. The operation of a chemical reactor unit always increases the value of chemical oxygen demand in the effluent stream. The behaviour of the total suspended solids is more complicated; in some cases, the operation of the CRU increases the total suspended solids. For a fixed value of the chemical oxygen demand in the influent stream, we show that both the percentage reduction in the total suspended solids and the chemical oxygen demand in the effluent stream are increasing functions of the soluble substrate in the feed. The same trends occur when the influent composition is fixed, and the disintegration rate inside the chemical reactor unit is increased. This leads to dichotomic behaviour, decreasing the total suspended solids increases the chemical oxygen demand in the effluent stream. There are two consequences of this behaviour. Firstly, there are some waste streams that can not be cleaned using the process configuration considered in this paper. Secondly, the imposition of a target value for the chemical oxygen demand in the effluent stream imposes a maximum achievable reduction in the total suspended solids in the biological reactor. Higher reductions can not be achieved without causing the chemical oxygen demand in the effluent stream to exceed the target value.

In recent times, steam ejectors have garnered significant interest among researchers due to their environmental friendliness and the utilization of low-grade energy sources. However, a key drawback of the ejector refrigeration cycle (ERC) is its relatively low coefficient of performance (COP). Understanding the behavior of ejectors under various operating conditions is crucial for addressing this concern. This study specifically focuses on investigating the flow characteristics of ejectors in the single-choking mode. Both dry steam model (DSM) and wet steam model (WSM) are employed to analyze and evaluate the performance in this study. Based on the findings, it is evident that the discharge pressure (DP) significantly influences the flow characteristics. With increasing DP, there is a decrease in the Mach number and liquid mass fraction (LMF) within the ejector, while the temperature distribution shows an upward trend. Additionally, as the DP increases, there is a notable decline in the entrainment ratio (ER) and production entropy. With an increase in the DP, both the DSM and WSM exhibit similar trends. However, in the DSM, the ER reaches zero at an earlier stage compared to the WSM. Specifically, when the DP rises from 5000 Pa to 5600 Pa, there is a 12.6 % increase in the production entropy in the WSM, while the DSM experiences a slightly higher increase of 12.9 %.

Natural pigment production represents an innovative and sustainable alternative to synthetic pigments. However, its industrial production to meet the global demand for pigments poses technological and economic challenges. In this work, a process design and simulation were conducted using SuperPro Designer to produce a blue natural pigment known as indigoidine, which is in high demand as a natural alternative to synthetic blue dyes in industries. The process design included upstream, bioreaction, and downstream processing to produce 113 tons per year of dry indigoidine. For the conception and design of the bioprocess, experimental data reported in the literature, such as kinetic and stoichiometric parameters, culture media, feeding strategy, and volumetric power input, were taken into account. The economic and profitability indicators of four scenarios were assessed based on a base scenario, which involved changing the typical stirred tank reactor to an airlift reactor, decreasing indigoidine recovery, and reducing biomass production. It was estimated that the use of an airlift reactor significantly improves the profitability of the bioprocess, while a 50 % decrease in biomass concentration (less than 40 g/L) significantly affected the profitability of the process. Finally, an equilibrium production point of around 56 tons per year was determined to balance total revenues with operational costs. This is the first work that offers valuable insights into the scaling-up of natural pigment indigoidine production using bacteria.

Since cooling load estimation directly impacts air conditioning control and chiller optimization, it is essential for increasing the energy efficiency of cooling systems. Machine learning outshines traditional regression analysis by efficiently managing vast datasets and discerning complex patterns influenced by various factors like occupancy, building materials, and meteorology. These capabilities greatly enhance building management and energy optimization. The primary objective of this study is to introduce a framework based on ML algorithms to accurately predict cooling loads in buildings. The Decision Tree model was chosen as the core algorithm for this purpose. Furthermore, as an innovative approach, four metaheuristic algorithms – namely, the Improved Arithmetic Optimization Algorithm, Prairie Dog Optimization, Covariance Matrix Adaptation Evolution Strategy, and Coyote Optimization Algorithm – were employed to enhance the predictive capabilities of the Decision Tree model. The dataset which utilized in this study derived from previous studies, the data comprised of eight input parameters, including Relative Compactness, Surface Area, Wall Area, Roof Area, Overall Height, Orientation, Glazing Area, and Glazing Area Distribution. With an astonishing R 2 value of 0.995 and a lowest Root Mean Square Error value of 0.660, the DTPD (DT + PDO) model performs exceptionally well. These astounding findings demonstrate the DTPD model’s unmatched precision in forecasting the results of cooling loads and point to its potential for useful implementation in actual building management situations. Properly predicting and managing cooling loads ensures that indoor environments remain comfortable and healthy for occupants. Maintaining optimal temperature and humidity levels not only enhances comfort but also supports good indoor air quality.

Biomass gasification technology is increasingly employed as an environmentally friendly energy source, primarily due to its minimal impact on the environment and its ability to mitigate pollution. This technology excels in producing gas with exceptionally high hydrogen content, making it a valuable source for both fuel and energy carriers. Hydrogen (H 2 ), renowned for its stability and lack of detrimental environmental effects, holds great significance in various applications related to energy utilization and sustainability. In the current work, wood sawdust was utilized as the biomass feedstock for syngas production. The research focused on examining the impact of introducing carbon dioxide (CO 2 ) and methane (CH 4 ) gases into the Gibbs reactors. The steam gasification process was modeled by the ASPEN Plus software, allowing for comprehensive analysis and simulation of the gasification reactions. According to the obtained results, the modeling performed in this study demonstrates good predictive capability when compared to the experimental data. It was shown that when the ratio of CO 2 to biomass (C/B) increases, the MFR (mass flow rates) of H 2 as well as CH 4 decrease, whereas the flow rates of CO 2 and carbon monoxide (CO) increase. These findings indicate the influence of the C/B ratio on the distribution of different gases within the gasification process. The reduction in MFR of hydrogen when transitioning from C/B = 0 to C/B = 1 in modes a and b is quantified as 17.51 % and 16.39 %, respectively. These percentages represent the magnitude of the decrease in hydrogen MFR for each specific mode when comparing two carbon dioxide to biomass ratios. When the CH 4 to biomass (M/B) ratio increases, the mass flow rates of H 2  exhibit a consistent upward trend, while the MFR of CO 2 displays a descending form. Specifically, when in the Gibbs reactor, M/B rises from 0 to 1 for modes a and b, the mass flow rates of H 2 experience significant increases of 265 % and 243 %, respectively. These findings underscore the direct relationship between the M/B ratio and hydrogen production, highlighting the potential for enhanced hydrogen yields with higher M/B ratios in the studied modes.

Originally, the Arrhenius parameters were used to estimate the rate of chemical reactions. This article aims to develop the optimal temperature to inhibit specific zero-order kinetic reactions. The model extends the use of the Arrhenius equation and heat capacity modeling to derive the optimal temperature solution. Specifically, the Arrhenius equation, which connects temperature to reaction rates, and the heat equation are formulated to create a comprehensive heat accumulation model. Analytical modeling is utilized through a derivative process to provide optimization. According to a case study of carotene oxidation, the derivative solution proposes −1.73 °C and can extend the reaction time by 206,160.29 days compared to a solution with no temperature change. The derivative solution also offers higher advantages in practical application than setting the lowest temperature limit due to the high initial energy requirement. The temperature derivative solution exhibits a global optimum property because of its high heat accumulation and slower kinetic reactions. These slower kinetic reactions can prevent reactant substances from deteriorating, making them valuable for maintaining a chemical’s shelf life. The temperature solutions offer valuable insights for devising an effective temperature strategy to inhibit specific chemical processes and verifying the relationship between temperature and heat accumulation with curvature.

A multi-objective optimization (MOO) technique to produce a low-density polyethylene (LDPE) is applied to address these two problems: increasing conversion and reducing operating cost (as the first optimization problem, P1) and increasing productivity and reducing operating cost (as the second optimization problem, P2). ASPEN Plus software was utilized for the model-based optimization by executing the MOO algorithm using the tubular reactor model. The multi-objective optimization of multi-objective Bonobo optimisers (MOBO-I, MOBO-II and MOBO-III) are utilised to solve the optimization problem. The performance matrices, including hypervolume, pure diversity, and distance, are used to decide on the best MOO method. An inequality constraint was introduced on the temperature of the reactor to prevent run-away. According to the findings of the study, the MOBO-II for Problems 1 and 2 was the most effective MOO strategy. The reason is that the solution set found represents the most accurate, diversified, and acceptable distribution points alongside the Pareto Front (PF) in terms of homogeneity. The minimum operating cost, the maximum conversion and productivity obtained by MOBO-II are Mil. RM/year 114.3, 31.45 %, Mil. RM/year 545.3, respectively.

The effect of a twisting parameter on heat transport in a square-sectioned twisted tube was studied. One definition of a twisting parameter is the ratio of the hydraulic diameter to the length of the tube at the point when it completed a full 360-degree twist. The twist parameters that were selected are 5, and they were compared to the Double Twisted Square Tube heat exchanger (DTSTHE). Transient flow was taken into account when evaluating the sets of Reynolds numbers. On the other hand, there are four different concentration volumes of nano fluid amounts of 0.005, 0.01, 0.025, and 0.04 for turbulent flow, and the Reynolds number ranges from 5,000 to 25,000. An examination was conducted into the effect of the twist parameter on the convective heat transfer coefficient through turbulent flow. The finite volume approach and the conventional − turbulence model were used to conduct numerical simulations of three-dimensional, steady-state incompressible flow in body-fitted coordinates. It was found that as the twist parameter is decreased, the heat transmission coefficient increases. What this means for the created span-wise swirling flow is unclear. With increasing distance from the tube center towards the walls, the whirling causes the cross-flow velocity vectors to increase. The boundary layer has good thermal properties because it thins out at the tube wall as the near-wall velocity increases. Internal thermal balance is further improved by spinning since it increases the mixing process. A higher Reynolds number, along with larger velocity components, results in a higher heat transfer coefficient.

A fundamental step in the education and scientific research processes is the final-year project for undergraduate students. In chemical engineering departments, the final year project has unique properties since it deals with the design and production of specific materials or chemicals. It represents a whole plant design. In the present work, a layout and tips are proposed for a typical final-year chemical engineering graduation project. Six chapters are suggested. Each chapter is given a main theme and subtitles in order to facilitate the writing process of the project. Chapter one represents an introduction to the importance of the material that was produced, material properties, production process, etc. In chapter two, material and energy balance calculations are addressed. Chapter three handled the equipment design. Cost and environmental assessments are discussed in Chapter four. The results of chapters two and three are compared with software outcomes, which can be collected in chapter five. Finally, the main results, conclusion, and recommendations for future work are proposed to be in Chapter six. Furthermore, tips and advice are addressed to assist students in the writing of a typical graduation project.