Abstract Solar collectors represent one of the most established and extensively utilized technologies for harnessing solar energy. In recent years, growing attention has been devoted to enhancing their efficiency through the incorporation of nanofluids, although significant progress in this area remains challenging. In this study, a comprehensive feasibility assessment of nanofluid integration into three commercial solar water heating systems (SWHSs), namely the flat plate solar collector (FPSC), the evacuated tube collector (ETC), and the compound parabolic collector (CPC) is conducted under the climatic conditions of Tunisia and Saudi Arabia. Simulation outcomes reveal that the incorporation of nanofluids significantly enhances the overall performance of solar water heating system, thereby shortening the payback period (PP) to approximately 4.45 years for Tunisia and 3.34 years for Saudi Arabia. The integration of nanofluids also contributed to a reduction in CO 2 emissions of up to 0.49 tons per year when used with electric auxiliary heaters. Hourly and daily analyses of system behavior confirmed stable operational performance, with collector thermal efficiencies reaching 60.62 % and a daily solar fraction exceeding 56.86 % during the coldest month at both locations. These findings demonstrate the potential of nanofluids to improve the techno-economic and environmental sustainability of solar water heating systems in diverse climatic regions.
Abstract This study presents a causal Physics Informed Neural Network framework for modeling and predicting the chemical kinetics of phenol synthesis governed by alkaline fusion and acidification reactions. The reaction mechanism involves sodium benzenesulfonate C 6 H 5 SO 3 Na, sodium hydroxide NaOH, sodium phenoxide C 6 H 5 ONa, sodium sulfite Na 2 SO 3 , hydrochloric acid HCl, phenol C 6 H 5 OH, water H 2 O, and sodium chloride NaCl, and is formulated as a nonlinear system of ordinary differential equations describing the time-dependent evolution of all chemical species. Stoichiometric constraints and governing physical laws are embedded directly into the loss function, enabling stable learning of concentration profiles and accurate estimation of kinetic parameters for stiff and strongly coupled reaction dynamics. The learned parameters show smooth convergence across multiple initial conditions and extended time horizons. At the same time, phase plane and phase space analyses reveal nonlinear species interactions, stability characteristics, and the transition from transient to equilibrium behavior. The proposed framework is robust, data efficient, and scalable, and is directly applicable to pharmaceutical manufacturing, polymer and resin production, and chemical intermediate synthesis, providing a reliable computational tool for stoichiometric analysis, stability assessment, and kinetic modeling of phenol synthesis.
Abstract Hydrogen is the foremost proficient energy carrier. It can be generated from various resources, among which water electrolysis stands out as an environmentally sustainable method capable of producing high-purity hydrogen. Furthermore, in view of the long viability and environmental effect, proton exchange membrane (PEM) water electrolysis has emerged as a technically feasible approach for hydrogen production using renewable energy sources, with oxygen as the only by-product and no associated carbon emissions. In this study, a dynamic model of a PEM electrolyzer is developed based on mole balance conservation at both the cathode and anode and energy conservation. The model incorporates key physical and electrochemical phenomena. The electrolyzer system is represented through four main sub-models: the anode, cathode, membrane, and voltage modules. Using the identified essential factors, the performances characteristics of the electrolyser are estimated at various operating temperatures. To reduce the required input voltage, the Taguchi optimization technique is employed to determine optimal process conditions. Key parameters considered in the optimization include operating temperature, anode and cathode pressures, membrane water content, membrane thickness and exchange current densities at both electrodes. Furthermore, the relative influence of these parameters on system performance is quantified using analysis of variance (ANOVA) and signal-to-noise ratio (SNR) analysis.
Abstract This paper investigates an application-oriented artificial intelligence framework for predicting the first-grade production percentage of ceramic tiles in an industrial roller kiln. The objective is to relate kiln operating conditions and biscuit physicochemical properties, including kiln-zone temperatures, firing cycle, biscuit flexural strength, biscuit moisture content, and final-product water absorption, to the quality outcome observed in production. Because data acquisition in industrial kiln operation is costly and limited, the study uses an expert-validated, uncertainty-aware training strategy rather than treating synthetic records as new independent plant measurements. After outlier removal, an independent real test set was separated and kept unchanged. Controlled perturbations within the normal industrial measurement uncertainty were applied only to the training subset: non-constant kiln-temperature variables were locally varied within the process tolerance confirmed by plant experts, while the reported uncertainty of the first-grade production percentage was considered within +/−0.7 percentage points for training robustness. Several baseline models, including FNN, CNN, LSTM, CatBoost, and the proposed LSTM-CatBoost framework, were evaluated using multiple regression metrics. The LSTM-CatBoost model achieved the strongest overall performance, with an R 2 of 0.946 on the test evaluation. The main contribution of this work is not the proposal of a new general-purpose machine-learning architecture, but the adaptation and evaluation of a practical data-driven quality-prediction framework for real roller-kiln operation under industrial data constraints.
The massive influx of Sargassum spp. along Caribbean coastlines has emerged as a critical environmental, socioeconomic, and public health issue, prompting the need for sustainable biomass valorization strategies. Although abundant, Sargassum remains underutilized in anaerobic digestion (AD) due to its compositional variability and the presence of inhibitory or recalcitrant compounds, which hinder biodegradability compared to conventional substrates such as manure or food waste. This study evaluated the technical feasibility of using Sargassum spp. as a feedstock in AD systems by simulating both mono-digestion and co-digestion scenarios. Four configurations were modeled under mesophilic conditions using Biodigestor-Pro v3.5, assuming a constant daily feed rate of 1 ton and incorporating operational parameters derived from the literature. The model estimated biogas yields, digestate mass flows, energy generation, and preliminary full-scale plant sizing, including feeding, digestion, gas handling, purification, and power extraction units. Results indicated that the highest outputs of biogas (27,528 m3/year), biofertilizer (730 ton/year) and energy (electricity: 53,290 kWh/year; heat: 99,280 kWh/year) were achieved under the co-digestion scenario of Sargassum and pig manure at a 50:50 ratio. Process efficiency could be enhanced through biomass pretreatment, inoculum selection, or performance optimization, which may also enable the recovery of additional high-value products beyond methane and fertilizer within an integrated processing chain. Taken together, these findings offer practical insights for the conceptual design of AD facilities in coastal regions and provide a basis for future, more comprehensive waste-to-energy assessments that integrate techno-economic and life-cycle perspectives.
Abstract The hybrid nanofluid has become an interesting field of the research due to having higher thermal conductance when compared to traditional common fluids. Due to their significance, two dimensional (2D) of CuO + Ag / water-based micropolar hybrid nanofluid flow on a stretching/shrinking sheet with Darcy Forchheimer, magnetohydrodynamics (MHD) and radiative impacts is accounted. Other physical effects like heat sink/source, micro material and chemical reactions, and porosity factor are also taken into consideration. Due to requirements of the used numerical methodology, the governing partial differential equations (PDEs) are changed to ordinary differential equations (ODEs) through similarity transformations. Later on, the ODEs are solved through shooting technique in Maple software. Due to raising duality in solutions, the stability of the solutions has been done in MATLAB through BVP4C code, where first solution is found as a feasible and stable solution and second one is unstable that rises due to high nonlinearity in equations. Some of the main findings show that an increment in volume fraction of nanoparticles and Forchheimer number leads to increase in the fluid velocity, while reverse trends are noticed within higher suction and micro material parameters. An increment in nanoparticles volume fraction, Forchheimer number, Biot number, radiation and magnetic parameters enhanced the temperature profiles.
To address the nonlinear coupling and performance conflicts among warpage deformation, volumetric shrinkage, and clamping force in injection molding, a collaborative framework integrating multi-model learning and multi-objective optimization is proposed. High-coverage simulation samples are first generated using Central Composite Design (CCD) and Latin Hypercube Sampling (LHS). On this basis, an ensemble prediction model is constructed by combining Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), with hyperparameters optimized via the Improved Runge-Kutta (IRUN) algorithm. Model interpretability is enhanced using SHapley Additive exPlanations (SHAP). In the optimization phase, multiple strategies - including good point set (GPS) initialization, Cauchy local perturbation, L & eacute;vy flight mutation, and adaptive PBI-based sorting - are incorporated into the Multi-objective Chaotic Game Optimization (MOCGO) algorithm to improve performance. Finally, the proposed method is validated through coupled simulations using Moldflow and Ansys. Warpage, shrinkage, and clamping force are reduced by 24.0 %, 6.3 % and 7.4 %, respectively, with improved stress distribution, demonstrating both effectiveness and engineering applicability.
This research analytically investigated the unstable magnetohydrodynamic (MHD) movement of a composite nanoliquid containing Cu3Zn2 and Co microelements in water (H2O) across an inclined penetrable plate, considering the impact of thermal radiation, and thermal diffusion. The mathematical model accounts for the consequences of thermal diffusion and chemical reaction, and provides insight into the effect of these factors on the flow properties. The primary goal is to explore how implications in the magnetic field, radiation, heat absorption, and thermal diffusion affect the flow velocity, temperature, and concentration outlines of the composite nanofluid. The study is of substantial importance for optimizing thermal transfer and mass transport mechanisms in various engineering applications, particularly in porous medium settings. The controlling nonlinear partial differential equations are converted into nondimensional form by employing dimensionless variables, and the derived system is analysed analytically utilizing the perturbation strategy. The findings are further validated through numerical computations in MATLAB. Closed-form solutions for velocity, temperature, concentration, skin friction factor, and Nusselt numeral are derived. The novel contribution of this research lies in the integration of hybrid nanoliquid properties with the impacts of aligned magnetic fields and thermal radiation, which enhances the thermal and solutal transmission capabilities. Outcomes reveal that enhancing the magnetic field intensity suppresses primary velocity, while higher radiation absorption and thermal diffusion promote greater fluid temperature. Furthermore, incorporating Cu3Zn2 and Co nanoparticles into the base fluid significantly improves the thermal performance of the hybrid nanoliquid, demonstrating its potential for use in advanced thermal management and energy systems.
Abstract This study investigates the unsteady magnetohydrodynamic (MHD) stagnation-point flow of Powell-Eyring nanofluid in a Darcy-Forchheimer permeable medium, including the influences of radiant heat, higher-order chemical reactions, suction, and bioconvective transport of gyrotactic microorganisms. The complex governing partial differential equations, which include the conservation rules of interconnected momentum, thermal energy, solute, and microorganisms, are reduced to a framework of nonlinear ordinary differential equations by similarity transformations. The equations were solved numerically using a suitable shooting method, together with the Runge-Kutta algorithm and the bvp4c MATLAB package. The results demonstrate that the factors of magnetic and porous resistance impede velocity while simultaneously enhancing temperature profiles due to Joule heating. The augmentation of chemical processes and Schmidt number results in a decrease in the thickness of the concentration boundary layer. In contrast, elevated bioconvective Lewis and Peclet numbers result in a reduction in microbial population near the surface. The evaluation of local skin friction, Nusselt number, Sherwood number, and motile microbe density ratio provides further empirical information. The core outcome of this study highlights that magnetic and porous resistances effectively regulate fluid velocity, while simultaneously elevating the thermal profile via Joule heating. The proposed theoretical model holds significant practical implications for the design of advanced bio-microsystems, smart electro-conductive coatings, and targeted drug delivery mechanisms where precise control of heat and microbial transport is essential.
The unsteady magnetohydrodynamic (MHD) flow of a Casson fluid through a porous medium has significant applications in biomedical engineering, particularly in modelling blood flow through tissues and small blood vessels. It also contributes to understanding heat and mass transfer phenomena, such as oxygen transport, drug delivery, and thermal regulation, in the presence of Soret and Dufour effects. Motivated by these applications, the present study investigates the unsteady mixed convection MHD flow of a non-Newtonian Casson fluid under the influence of Soret and Dufour effects. The fluid flow past an exponentially accelerating vertical porous plate embedded in a porous medium is analyzed under ramped wall temperature and concentration conditions. Furthermore, the model incorporates the effects of chemical reaction, thermal radiation, Joule heating, and viscous dissipation. The governing coupled, nonlinear, and dimensionless differential equations are solved numerically using the finite difference method. The computational results are presented graphically to illustrate the effects of the governing parameters on the velocity, temperature, and concentration fields, while the skin-friction coefficient, Nusselt number, and Sherwood number are reported in tabular form. The results reveal that viscous dissipation, heat generation, thermal radiation, and the Dufour effect enhance both the velocity and temperature distributions of the Casson fluid. Likewise, increasing porosity and buoyancy parameters accelerates the fluid motion, whereas the Casson parameter and magnetic field strength suppress the flow velocity. Moreover, the Soret effect increases both the velocity and concentration profiles, while chemical reactions reduce the concentration field. It is also observed that higher values of viscous dissipation, Dufour effect, and heat generation lead to a decrease in the Nusselt number and skin-friction coefficient.
To investigate how the chemistry of an electrolyte impacts the surface behaviour and electro catalytic performance of copper electrodes during the electrochemical reduction of carbon dioxide (CO 2 ), experiments were performed using varying concentrations of potassium hydrogen carbonate (KHCO 3 ) in near neutral solutions, at open circuit and through the use of several types of electrochemical techniques; specifically, linear sweep voltammetry, chronoamperometry, and measurements of open circuit potential. The results demonstrate that the open circuit potential of copper immersed in these solutions is shifted upward when exposed to CO 2 as compared to when exposed to nitrogen, while increased cathodic current density was observed. These results confirm that the presence of CO 2 causes the formation of a conditioned electrode/solution interface on copper. Increased KHCO 3 solution concentrations result in higher cathodic current densities and better stability of the cathodic process, indicating that the ability of the electrolyte to act as a buffer, or to increase the strength of the ions within it, are important factors controlling the rate and efficiency of this reaction.
This study investigates coupled heat and mass transfer in mixed convection of Fe 3 O 4 –ethylene glycol and MoS 2 –ethylene glycol nanofluids within a porous cylindrical annulus, a geometry that fundamentally alters nanoparticle migration compared with planar configurations. The model simultaneously incorporates activation energy controlled chemical reactions, temperature-dependent viscosity, thermo-diffusion (Soret effect), thermal radiation, and radiation absorption. Numerical results reveal that thermo-diffusion redistributes nanoparticles away from both annular walls, leading to a reduction in Sherwood number by up to 18 % even when thermal gradients intensify, while thermal radiation enhances the temperature field and increases the Nusselt number by approximately 15 %. Radiation absorption raises the bulk temperature without a proportional increase in nanoparticle flux, confirming a decoupling between heat and mass transport. Increasing activation energy promotes nanoparticle accumulation near the walls but reduces heat transfer, causing the Nusselt number to decrease by nearly 12 %. The presence of dual confining walls further produces asymmetric heat and mass transfer rates between the inner and outer cylinders. These findings demonstrate that annular nanofluid transport cannot be inferred from single-surface models and provide new insights relevant to annular heat exchangers, reactor cooling channels, and energy-storage systems.
The ozone (O 3 ) is a highly reactive substance that reacts rapidly with surrounding molecules and it is one of the most important environmental pollutants. The decomposition of O 3 to oxygen molecules is important pathway to decrease the O 3 amount because O 3 disrupts the cycle of many chemical reactions. In this study, the capacities of Cr–C 46 , Cr–Ge 46 , Ti–Si 60 , Ti–C 60 and Ti–B 30 N 30 nanocages for decomposition of O 3 to oxygen molecules are investigated by PW91PW91/cc-pVQZ and M06-2X/6-311+G (2d, 2p) methods. The possible pathways of decomposition of O 3 on Cr–C 46 , Cr–Ge 46 , Ti–Si 60 , Ti–C 60 and Ti–B 30 N 30 nanocages are examined to propose the acceptable mechanism from thermodynamic viewpoint. The calculated E activation and ΔG reaction of reaction steps of decomposition of O 3 by LH and ER pathways on Cr–C 46 , Cr–Ge 46 , Ti–Si 60 , Ti–C 60 and Ti–B 30 N 30 nanocages are compared with metal-based catalysts. The releasing the O 2 is rate-determining step by highest E activation on Cr–C 46 , Cr–Ge 46 , Ti–Si 60 , Ti–C 60 and Ti–B 30 N 30 . The new nano-catalysts (Cr and Ti doped Si and BN nanocages) for decomposition of O 3 to oxygen molecules with high efficiency are proposed to use in industry.
This study presents a process modelling and design approach to support the transition of methylene blue dye removal using a magnetic clay@Fe 3 O 4 composite in continuous systems. Aspen Adsorption software was employed to simulate breakthrough behaviour under varying influent concentrations, flowrates, and bed heights, using experimentally derived isotherm data and estimated mass transfer parameters. The model captured general adsorption dynamics, although prediction extended breakthrough curves than experimental observations. To optimize resource efficiency, a two-stage adsorber design was evaluated for different removal targets. Results showed minimal adsorbent savings (0.041 %) for the high-affinity clay@Fe 3 O 4 system, indicating limited benefit of staging when adsorbent performance is already high. However, total adsorbent demand increased sharply with removal targets, requiring over six times more material to achieve 99 % versus 80 % removal, highlighting the operational cost implications of stringent effluents standards. By integrating dynamic column simulation with systematic process design, this study demonstrates a practical framework for translating bench-scale adsorbent development into industrial practical wastewater treatment configurations, addressing key challenges in performance prediction and resource optimization.
Bioremediation is a sustainable and promising technology for the remediation of different kinds of pollutants, such as heavy metals, dyes, antibiotics, microplastics, and other contaminants present in industrial wastewater. These emergent toxic pollutants have adverse effects on human health. However, different conventional technologies such as filtration, ion exchange, precipitation, etc., are used to remove contaminants such as heavy metals and organic and inorganic pollutants. In this direction, the application of green materials, nanoparticles, and their composites, phyco-remediation, mycoremediation, and different kinds of bio(nano) sorbents such as biochar, hydrochar, chitin, and chitosan, etc., are used for the removal of hazardous pollutants from industrial wastewater. Green bio(nano) sorbent materials are eco-friendly, sustainable in nature, and offer enhanced adsorption efficiency and selectivity as compared to the conventional mode of wastewater treatment. A techno-economic analysis and a circular economy analysis are required for assessing bioremediation. The future perspective and challenges are addressed for the implementation of bioremediation-based technology. Integration of other techniques, such as artificial intelligence, advanced machine learning, and the Internet of Things (IoT) is used by researchers for the bioremediation of industrial wastewater. Thus, the application of green (nano)materials for industrial wastewater treatment is a sustainable environmental solution.
Prolong dead time is quite common for industrial processes due to transportation lag, cascaded time constant elements like distillation column, recycler, demineralization tank etc. In addition, presence of computational and communication delay, data conversion phase, time taking measurements also contribute considerable time delay. In such cases, Smith predictor (SP) based control scheme is recommended to ascertain desirable close-loop behaviour. However, SP performance suffers considerably with model uncertainty and measurement noise. Consequently, different SP augmentation schemes are proposed, referred as modified Smith predictor (MSP). A good number of publications on MSP designs are reported by the researchers over the last two decades. Various model based MSP schemes are explored here to point out their suitability especially with delay dominated integrating and unstable processes due to their challenging behaviour. To ascertain improved set point tracking as well as efficient disturbance rejection multiple number of controllers are being utilized in MSP design. Choice of appropriate filter plays crucial role towards performance enhancement of MSP methodologies. Close-loop performance analysis among the widely reported MSP schemes are made with integrating plus time delay (IPTD), unstable first-order plus time delay (UFOPTD), integrating first-order plus time delay (IFOPTD), double integrating plus time delay (DIPTD) and unstable second-order plus time delay (USOPTD) processes. In addition to graphical responses, quantitative performance indices along with stability margins are also computed. In addition, efficacy of selective MSP schemes is also validated through real-time experimentation.
The supply of energy to all the sectors is difficult nowadays due to increase of its demand and depletion of fossil fuels. Fossil fuels have to be replaced by an alternative energy sources due to creation of ill environment by fossil fuels. Bioethanol has developed as one of the leading renewable fuels with through generations. In the first-generation, Bioethanol is produced from food crops like corn or sugarcane, however it faced numerous obstacles such as food security and land competition. In the second-generation, bioethanol derived from lignocellulosic biomass to replace the first-generation feed stocks. This, unfortunately, also faced the same challenge. Feedstock recalcitrance, high processing costs, and limited commercial viability. Third-generation bioethanol, based on engineered microbial or algal systems, represent a more sustainable pathway with lower land use and higher yields, though it still faces constrains in the form of high energy inputs and the need for advanced bioreactor design. Fourth generation bioethanol addresses the issues of all three generations of bioethanol production. The main findings of these processes include genetic engineering of microbial strains, improved enzymatic hydrolysis, and integrated bio refinery concepts that enhance efficiency and reduce waste. Technologically, Bioethanol advances highlight the optimization challenges between conversion efficiency, resource availability, and cost-effectiveness. Environmentally, adoption of Bioethanol paves the way to reduced greenhouse gas emissions, increased circular use of biomass, and reduced dependence on fossil fuels.
The trying to propose the novel catalysts to increase the Claus reactions in order to remove the SO 2 and H 2 S with high performances is very important. Here, the capacities of metal doped nanostructures as catalysts for SO 2 hydro-desulfurization to produce the H 2 S are examined. The possible mechanisms and reactions pathways for SO 2 hydro-desulfurization to H 2 S production on Mn doped nanostructures are investigated. The important species for SO 2 hydro-desulfurization are adsorbed on Mn atoms of nanostructures. The H 2 S molecule is generated on Mn doped nanostructures through following reaction steps: SO 2 * → SO* → S* → SH* → H 2 S. The H 2 S is desorbed from surfaces of metal doped nanostructures, easily. The final performances of used catalysts for SO 2 hydro-desulfurization to H 2 S production are changed in this order: Mn-SiNT(7, 0) > Mn-BPNT(7, 0) > Mn-CNT(7, 0) > Mn-Si 60 > Mn-B 30 P 30 > Mn-C 60 . Finally, the Mn-SiNT(7, 0), Mn-CNT(7, 0) and Mn-BPNT(7, 0) as catalysts have effective ability for SO 2 hydro-desulfurization to H 2 S production from thermodynamic view point.
