Search Results (343)

High outdoor PM_2.5 levels in Chinese cities pose significant challenges to maintaining healthy indoor air quality in residential buildings, where mechanical ventilation systems are increasingly adopted for pollution control. In this paper, to control the indoor PM_2.5 concentration, a mass balance equation for the non-uniform mixing model has been established to calculate the filter efficiency. This study aims to optimize PM_2.5 pollution control in residential buildings through mechanical ventilation systems by evaluating the synergistic effects of filter efficiency and ventilation air flow rates under high outdoor PM_2.5 conditions. Field measurements and numerical calculations were conducted to monitor indoor and outdoor PM_2.5 concentrations. Results showed that, When outdoor PM_2.5 concentrations remain below 100 μg/m³, an air exchange rate of 3 h⁻¹ effectively maintains indoor PM_2.5 levels below 35 μg/m³ for M6-F8 air filters. Experimental data demonstrate that when a fresh air system equipped with H10 filters operates at an outdoor PM_2.5 concentration of 150 μg/m³, the corresponding optimal ventilation rate is 0.45 h⁻¹. Increasing the mechanical ventilation rate to 1 h⁻¹ enables the system to effectively handle higher outdoor concentrations up to 176 μg/m³. Under severe pollution scenarios with outdoor PM_2.5 concentrations reaching 250 μg/m³, the air exchange rate should be further increased to 1.65 h⁻¹ to maintain indoor PM_2.5 concentrations within acceptable limits. This study provides practical insights for improving residential indoor air quality under high outdoor PM_2.5 conditions in Chinese cities. Full article

This work systematically addresses the dual challenges of non-inertial dynamic coupling and kinematic constraint redundancy encountered in dynamic modeling of serial–parallel–serial hybrid robotic mechanisms, and proposes an improved Newton–Euler modeling method with constraint compensation. Taking the Skiing Simulation Platform with 6-DOF as the research mechanism, the inverse kinematic model of the closed-chain mechanism is established through $G_F$ set theory, with explicit analytical expressions derived for the motion parameters of limb mass centers. Introducing a principal inertial coordinate system into the dynamics equations, a recursive algorithm incorporating force/moment coupling terms is developed. Numerical simulations reveal a 9.25% periodic deviation in joint moments using conventional methods. Through analysis of the mechanism’s intrinsic properties, it is identified that the lack of angular momentum conservation constraints on the end-effector in non-inertial frames leads to system controllability degradation. Accordingly, a constraint compensation strategy is proposed: establishing linearly independent differential algebraic equations supplemented with momentum/angular momentum balance equations for the end platform. Co-Simulation results demonstrate that the optimized model reduces the maximum relative error of actuator joint moments to 0.98%, and maintains numerical stability across the entire configuration space. The constraint compensation framework provides a universal solution for dynamics modeling of complex closed-chain mechanisms, validated through applications in flight simulators and automotive driving simulators. Full article

Fed-batch cultivation is a widely used strategy for microbial lipid production, offering flexibility in nutrient control and the potential for high lipid productivity. However, optimizing feeding strategies remains a complex challenge, as it depends on multiple factors, including strain-specific metabolism and process limitations. In this study, we developed a computational framework based on dynamic flux balance analysis and small-scale metabolic models to evaluate and optimize lipid production in Rhodosporidium toruloides strains. We proposed equations to estimate both the carbon and energy source mass feed rate ($F_{in}·s_r$) and its concentration in the feed ($s_r$) based on lipid accumulation targets, and defined minimum feeding flow rate ($F_{in}$) according to process duration. We then assessed the impact of these parameters on commonly used bioprocess metrics—lipid yield, titer, productivity, and intracellular accumulation—across wild-type and engineered strains. Our results showed that the selection of $F_{in}·s_r$ was strongly strain-dependent and significantly influenced strain performance. Moreover, for a given $F_{in}·s_r$, the specific values of $s_r$, and the resulting $F_{in}$, had distinct and non-equivalent effects on performance metrics. This methodology enables the rational pre-selection of feeding strategies and strains, improving resource efficiency and reducing the probability of failed experiments. Full article

Bone remodeling is a dynamic biological process that preserves bone strength and structure through the coordinated actions of osteoblasts, osteoclasts, osteocytes, and bone mass density. Traditional models based on ordinary differential equations often fail to capture the memory-dependent nature of these interactions. In this study, we propose a novel mathematical model of bone remodeling using the Atangana–Baleanu–Caputo fractional derivative, which accounts for the non-local and hereditary characteristics of biological systems. The model introduces fractional-order dynamics into a previously established ODE framework while maintaining the intrinsic symmetry between bone-forming and bone-resorbing mechanisms, as well as the balance mediated by porosity-related feedback. We establish the existence, uniqueness, and positivity of solutions, and analyze the equilibrium points and their global stability using a Lyapunov function. Numerical simulations under various fractional orders demonstrate symmetric convergence toward equilibrium across all biological variables. The results confirm that fractional-order modeling provides a more accurate and balanced representation of bone remodeling and reveal the underlying symmetry in the regulation of bone tissue. This work contributes to the growing use of fractional calculus in modeling physiological processes and highlights the importance of symmetry in both mathematical structure and biological behavior. Full article

In the age of widespread renewable energy integration, coal-fired power plants are transitioning from a primary baseload role to a more flexible peak-shaving capacity. Under frequent load changes, the thermal efficiency will significantly decrease. In order to achieve efficient dynamic operation, this study proposes a comprehensive mechanical model of a 300 MW drum-type boiler. Based on the Modelica/DYMOLA platform, the multi-domain equations describing energy and mass balance are programmed and solved. A comprehensive evaluation of the energy transformation within the boiler’s heat exchange components was performed. Utilizing the principles of exergy analysis, this study investigates how fluctuating operational conditions impact the energy dynamics and exergy losses in the drum and heating surfaces. Steady-state simulation reveals that the evaporator and superheater units account for 81.3% of total exergy destruction. Dynamic process analysis shows that the thermal inertia induced by the drum wall results in a significant delay in heat transfer quantity, with a dynamic period of up to 5000 s. The water wall exhibits the highest total dynamic exergy destruction at 9.5 GJ, with a destruction rate of 7.9–8.5 times higher than other components. Full article

Harmful heavy metals (HHMs) in marine sediments pose significant ecological and human health risks. This research developed a novel one-dimensional mathematical model to investigate the desorption rates and background concentrations ($C_{bg}$) of HHMs in cohesive sediments of coastal environments, using Cartagena Bay (CB), Colombia, as a reference for estuarine systems. The model integrates mass balance and molecular diffusion equations incorporating porosity and tortuosity. Both the particulate and dissolved phases of HHMs were considered. Numerical experiments were conducted over 28 years with a daily time step, simulating four primary hydrodynamic processes: molecular diffusion, desorption, sedimentation, and turbulent water exchange. The spatiotemporal evolution of $C_{bg}$ provides valuable insights for sediment modeling, policy development, and advancing the understanding of HHM pollution in sediments. Results of the model align closely with empirical data from CB, demonstrating its applicability not only to local conditions but also to similar contaminated areas through a generalized approach. This model can be used as a reliable computational tool for managing coastal environments. Full article

The seepage diffusion of cement grouting materials into a sandy medium is influenced by the skeleton’s adsorption and the pore channels’ tortuosity, resulting in heterogeneous retention of cement particles during migration. This study established a theoretical model for the filtration coefficient based on the mass balance equation and linear filtration law. Grouting tests were conducted to determine the density of the cement slurry at various diffusion positions, and the filtration coefficient was calculated using the theoretical model. Results indicate that the filtration coefficient varies dynamically along the diffusion distance rather than remaining constant. The surface filtration range of Grade 42.5 Portland Cement slurry in sample S1 is approximately 30 cm, with a final diffusion distance of 190 cm. In contrast, the surface filtration ranges for the 800 mesh superfine cement in S2 and the 1250 mesh superfine cement in S3 are less than 10 cm, resulting in final diffusion distances of 69 cm and 87 cm, respectively. This demonstrates that a longer surface filtration range in the sand sample corresponds to a farther final diffusion distance of the slurry. Additionally, a larger ratio of sand pore diameter to cement particle size results in a smaller filtration coefficient and a greater slurry diffusion distance. Under a constant water–cement ratio, smaller cement particle sizes are associated with decreased slurry fluidity, which reduces the diffusion of cement slurry within the sandy medium. The research findings provide valuable insights for designing borehole spacing in grouting treatment for sandy media. Full article

During crude oil exploration and extraction, the presence of H₂S not only poses a threat to operational safety but also accelerates equipment corrosion, highlighting the urgent need for efficient and cost-effective processing solutions. This study employs a coupled numerical simulation approach that integrates computational fluid dynamics (CFD) and population balance models (PBM) to systematically investigate the multiphase flow characteristics within SK static mixers. By embedding mass transfer rates and reaction kinetics equations for hydrogen sulfide and sodium hypochlorite into the Euler-Euler multiphase flow model using user-defined functions (UDFs), the effects of equipment structure on the efficiency of the crude oil desulfurization process are examined. The results indicate that the optimized SK static mixer (with 15 elements, an aspect ratio of 1, and a twist angle of 90°) achieves an H₂S removal efficiency of 72.02%, which is 18.84 times greater than that of conventional empty tube reactors. Additionally, the micro-mixing time is reduced to 0.001 s, and the coefficient of variation (CoV) decreases to 0.21, while maintaining acceptable pressure drop levels. Using the CFD-PBM model, the dispersion behavior of droplets within the static mixer is investigated. The results show that the diameter of the inlet pipe significantly affects droplet dispersion; smaller diameters (0.1 and 1 mm) enhance droplet breakup through increased shear force and turbulence effects. The findings of this study provide theoretical support for optimizing crude oil desulfurization processes and are of significant importance for enhancing the economic efficiency and safety of crude oil extraction operations. Full article

This paper focuses on improvement effects on soil foundations under dynamic compaction (DC). Firstly, a confocal ellipsoidal densification model (CEDM) composed of a heavy compacted zone (HCZ) and a weak compacted zone (WCZ) was proposed to describe the subarea characteristic of an improvement range. Next, based on a confocal assumption of HCZ and WCZ ellipses, a mass balance equation considering changes in soil dry density in different compacted zones was established for solving the ellipsoidal parameters. Then, a designed laboratory test was conducted and a two-dimensional (2D) finite element model (FEM) established. The simulated crater depth and dynamic stress agreed well with testing results, confirming that the established FEM could be used for investigating the DC process. Finally, the applicability of the solution procedure for the proposed CEDM was verified. The predicted HCZ and WCZ were in close agreement with the simulated results, indicating that the proposed CEDM could be used for estimating the soil improvement range. With increases in tamping times, the HCZ ellipse moved down in the vertical direction without volumetric expansion, while the WCZ ellipse expanded along the depth and lateral directions. These findings may offer some guidelines for research into improvement effects on soil foundation under DC. Full article

Melt crystallization is a promising separation technique that produces ultra-high-purity products while consuming less energy and generating lower CO₂ emissions than conventional methods. However, accurately modeling melt crystallization is challenging due to significant non-idealities and complex phase equilibria in multicomponent systems. This study develops and evaluates two neural network-based surrogate models for acrylic acid melt crystallization: a stand-alone (black-box) model and a thermodynamically guided (hybrid) model. The hybrid model incorporates UNIQUAC-based solid–liquid equilibrium constraints into the learning process. This framework combines first-principles thermodynamic knowledge—particularly activity coefficient calculations and mass balance equations—with multi-output regression to predict key process variables. Both models are rigorously tested for interpolation and extrapolation, with the hybrid approach demonstrating superior accuracy even under operating conditions significantly outside the training domain. Further analysis reveals the critical importance of accurate solid–liquid equilibrium (SLE) data for thermodynamic parameterization. A final case study illustrates how the hybrid approach can quickly explore feasible operating regions while adhering to strict product purity targets. These findings confirm that integrating mechanistic constraints into neural networks significantly enhances predictive accuracy, especially when processes deviate from nominal conditions, providing a practical framework for designing and optimizing industrial-scale melt crystallization processes. Full article

In this paper, our primary objective is to develop a robust and efficient higher-order structure-preserving algorithm for the numerical solution of the two-dimensional nonlinear spatial fractional Schrödinger equation. This equation, which incorporates fractional derivatives, poses significant challenges due to its non-local nature and nonlinearity, making it essential to design numerical methods that not only achieve high accuracy but also preserve the intrinsic physical and mathematical properties of the system. To address these challenges, we employ the scalar auxiliary variable (SAV) method, a powerful technique known for its ability to maintain energy stability and simplify the treatment of nonlinear terms. Combined with the composite Simpson’s formula for numerical integration, which ensures high precision in approximating integrals, and a fourth-order numerical differential formula for discretizing the Riesz derivative, we construct a highly effective finite difference scheme. This scheme is designed to balance computational efficiency with numerical accuracy, making it suitable for long-time simulations. Furthermore, we rigorously analyze the conserving properties of the numerical solution, including mass and energy conservation, which are critical for ensuring the physical relevance and stability of the results. Full article

A mathematical model was developed for the dynamic and static simulation of a continuous ethanol production process in a tower bioreactor packed with yeast cells immobilized in citrus pectin gel. To avoid accumulation of CO₂ gas during the bioprocess, a vertical fixed bed bioreactor with a working volume of 0.245 L, divided into four stages and equipped with external gas–liquid separators was used. The performance of the bioreactor was evaluated through continuous fermentations using feed medium (sugarcane juice) with substrate concentrations of 161.4 and 312.5 g/L, temperature of 30 °C, pH 4.0 and hydraulic residence times of 5 and 6 h. The developed mathematical model takes into account mass flow by convection and dispersion axial, external and internal mass transfer to/within particle, Contois kinetics for cell growth with inhibition terms, cell death, and substrate consumption for cell maintenance. The partial differential equations regarding cell, substrate and product mass balances in the solid and fluid phase were solved by numerical methods. The calculated profiles of state variables in the fluid phase agreed satisfactorily with the experimental data. The diffusional resistances within particles concerning the substrate consumption rate were not significant, resulting in calculated values of the effectiveness factor close to one. Full article

A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility. Full article

The growing demand for sustainable water management solutions has prompted the development of membrane brine concentration (MBC) technologies, particularly in the context of desalination and minimum liquid discharge (MLD) applications. This study presents a simple model of high-pressure nanofiltration (HPNF) for MBC. The model integrates reverse osmosis (RO) transport equations with mass balance equations, thereby enabling acceptable predictions of water flux and total dissolved solids (TDS) concentration. Considering the limitations of the pilot plant data, the model showed reasonable accuracy in predicting flux and TDS, with R² values above 0.99. The simulation results demonstrated that an increase in feed flow rate improves flux but raises specific energy consumption (SEC) and reduces recovery. In contrast, an increase in feed pressure results in an increased recovery and brine concentration. Increasing feed TDS decreases flux, recovery, and final brine TDS and increases SEC. Response surface methodology (RSM) was employed to optimize process performance across multiple criteria, optimizing flux, SEC, recovery, and final brine concentration. The optimal feed flow rate and pressure vary depending on the criteria in the improvement scenarios, underscoring the importance of systematic process improvement. Full article

Hydrogen has been presented as a promising energy vector in decarbonized economies. Its singular properties can affect important aspects of industrial flames, such as the temperature, emissions, and radiative/convective energy transfer balance, thus requiring in-depth studies to optimize combustion processes using this fuel isolate or in combination with other renewable alternatives. This work aims to conduct a detailed numerical analysis of temperatures and gas emissions in the combustion of biomethane enriched with different proportions of hydrogen, with the intent to contribute to the understanding of the impacts of this natural gas surrogate on practical combustion applications. RANS k-$\omega$ and k-$ϵ$ turbulence models were combined with the GRI Mech 3.0, San Diego, and USC mechanisms using the ANSYS-Fluent 2024-R2 softwareto evaluate its performance regarding flame prediction. The Moss–Brookes model was adopted to predict soot formation for the methane flames by solving transport equations for normalized radical nuclei concentration and the soot mass fraction. The Discrete Ordinates (DOs) method with gray band model was applied to solve the Radiation Transfer Equation (RTE). The results of the experiments and numerical simulations highlight the importance of carefully selecting turbulence and chemical kinetics models for an accurate representation of real-scale industrial burners. Relative mean errors of 1.5% and 6.0% were registered for temperature and pollutants predictions, respectively, with the USD kinetics scheme and k-$omega$ turbulence model presenting the most accurate results. The operational impacts of hydrogen enrichment of biomethane flames were accessed for a practical combustion system. With 15% of hydrogen blending, the obtained results indicate a 73% penalty in CO emissions, an increase of 6% in NO emissions, and a 34 K flame temperature increase. Also, a reduction in flame radiation due to hydrogen enrichment was observed for hydrogen concentrations above 20%, a behavior that can affect practical combustion systems such as those in glass and other ceramics industries. Full article