Engineering

Abstract: An entropy-based solubility parameter-translated Soave-Redlich-Kwong equation of state (eSPT-SRK EoS) was developed that incorporates two correction parameters which can be linearly correlated with the critical compressibility factor of pure substances. The correlated results gave an average relative deviation (ARD) value of 5.4 % at the critical density for a database of 28 widely-used chemicals. Estimation of liquid densities at standard temperature and pressure conditions gave ARD values of 5.8 % compared with the original SRK EoS of 22.2 %. The eSPT-SRK EoS was applied to calculate thermodynamic properties (entropy, fugacity, cohesive energy density) and was found to be reliable for estimating entropy-based Hansen-type solubility parameters (eHSP). The eSPT-SRK EoS was compared with a corrected form of the entropy-based solubility parameter-traslated Peng-Robinson EoS (eSPT-PR EoS) and both functional forms were found to give reliable eHSP values. Therefore, the formulation methodology can be applied to other cubic equations of state for specialized fluid mixtures.

Abstract: Catalyst deactivation shifts the optimal operating region of exothermic fixed-bed reactors, yet most reactor digital twins focus on monitoring rather than catalyst-state-aware operating decisions. This work presents a self-optimizing digital twin integrating a physics-based reactor model, a moving-window constrained activity estimator, and a target-optimization layer, demonstrated through simulation for o-xylene oxidation to phthalic anhydride in a vanadia-titania heat-exchanged fixed-bed reactor. Sparse axial temperature and conversion measurements are reconciled to estimate an axial catalyst activity profile; gas and coolant inlet temperatures are then updated subject to a hot-spot safety constraint. The estimator achieved an activity-profile RMSE of 0.075, an outlet-conversion RMSE of 0.99 percentage points, and an outlet-temperature RMSE of 1.85K. Under the baseline noisy-measurement scenario, estimated-activity optimization raised the mean phthalic anhydride yield from 46.3% under fixed targets to 61.9%, within 0.14 percentage points of the true-activity optimum, while maintaining the maximum reactor temperature below 730K. This corresponds to recovering approximately 99.1% of the yield improvement available with perfect catalyst-state knowledge. The policy remained superior to fixed-target operation across all tested noise levels, sensor configurations, and kinetic pre-exponential perturbations, although plant validation is still required to quantify structural model error. The findings demonstrate the value of linking catalyst-state estimation to operating-target adaptation in a reproducible catalytic-reactor digital-twin workflow.

Abstract: The rapid growth of electric mobility, renewable energy storage, and portable electronics has sharply increased global lithium demand, highlighting the environmental and socio economic drawbacks of conventional extraction methods such as hard rock mining and brine evaporation. These processes are land intensive, slow, water consumptive, and carbon intensive, underscoring the need for next generation materials that enable selective, circular and sustainable lithium recovery. Zeolite based adsorbents have emerged as strong candidates, due to their crystalline frameworks, tunable pore architectures, ion exchange functionality, and exceptional thermal and chemical stability. This review covers recent advances in natural and synthetic zeolites, and zeolite-based composites for lithium capture, with emphasis on guiding design principles governing Li⁺ adsorption capacity and selectivity, transport behavior, and adsorption mechanisms across diverse feedstocks such as brines, geothermal fluids, seawaters, and battery recycling leachates. Lastly, we discuss current challenges and emerging opportunities that will guide future research aimed at advancing zeolite-based adsorbents toward sustainable, next-generation lithium recovery technologies.

Abstract: Catalyst activity loss reduces both the performance and operating flexibility of industrial sulfur dioxide converters, yet its consequences are rarely assessed beyond conversion decline. This work develops an activity-loss vulnerability framework for a four-bed double- contact SO₂ converter model evaluated against an industrial fresh-catalyst benchmark and applies it to four prescribed activity scenarios (a = 1.0, 0.8, 0.6, 0.4). At the reference inlet-temperature policy, reducing activity from a = 1.0 to a = 0.4 lowered conversion from 99.758% to 96.812%, increased outlet SO₂ slip from 230 to 2960 ppmv, and raised the hotspot from 613.7 to 660.3 °C, exceeding the adopted illustrative limit of 650 °C. Sensitivity, vulnerability, hotspot-risk, and feasible-region maps show that prescribed activity loss progressively shrinks the permissible operating envelope and creates a coupled productivity–emissions–thermal-safety tradeoff. A non-uniform activity profile at the same mean activity as uniform a = 0.6 produced a hotspot 9.3 °C higher, demonstrating that average activity alone is insufficient for thermal-risk assessment. Finally, a scenario-relative Operating Efficiency Reduction Index (OERI) integrates conversion loss, SO₂-slip increase, and thermal-margin loss into an illustrative scenario-screening score. The results show that catalyst activity loss should be assessed as a coupled performance, emissions, and operational-vulnerability problem rather than conversion decline alone.

Abstract: Diesel fuel forensic analysis has been accomplished to ensure the cut obtained from fractional distillation yielded the desired product. The analysis was achieved via Fourier Transform Infrared Spectrometry and Gas Chromatography Mass Spectrometry techniques. Crude oil sample obtained from Tema Oil Refinery was emulsified with sea water. The mixture was cleaned from the seawater and distilled. The cut received from the distillation at about 265 oC and a pure diesel standard sample were sent to Kwame Nkrumah University of Science and Technology chemistry laboratory for the FTIR and GC-MS analysis. The peaks provided by the GC-MS of components from the cut were compared with the peaks of the components of the pure diesel sample. Furthermore, the chemical compositions of the cut were compared with the chemical compositions of diesel samples published by other researchers. Again, the functional groups of the cuts produced by the GC-MS were related to the diesel functional groups retrieved from the publications of other researchers. Additionally, the spectrum of the cuts produced by FTIR were overlayed on the pure diesel sample spectrum for comparison. Based on the similarities of the GC-MS information and the FTIR information, it was concluded that the cut was a diesel.

Abstract: The leaching of chalcopyrite in sulfate or chloride media has been proposed to occur through a combined non-oxidative/oxidative mechanism, where H₂S forms an intermediary species, concurrently with direct oxidative leaching. Some studies have noted that elevated concentrations of Cu(II) improve chalcopyrite leaching in sulfate media. In this study, the role of Cu(II) was investigated in the non-oxidative/oxidative process through electrochemical tests on a chalcopyrite electrode, supported by bulk leach tests. The results of the electrochemical tests are consistent with the formation of H₂S through non-oxidative leaching of chalcopyrite. The H₂S subsequently reacts with Cu(II) to form intermediate cuprous sulfide species, which can be readily oxidized by dissolved oxygen. The bulk leach tests point to a synergy between Cu(II) and O₂, further confirming the catalytic role of Cu(II). The findings support the feasibility of running heap leaching of chalcopyrite-rich ores at elevated copper concentrations in either chloride or sulfate systems.

Abstract: This research aims to study by numerical simulation the optimization of a fed batch bioreactor (FBBR). The main characteristic of this reactor is that it used to produce biomass concentration (X) or a specific product concentration (P). The study of the FBBR is carried out in order to evidence the influence of the stirrer kind on the biomass growing duration inside the reactor. So, in this study, the variable to be minimized is the biomass growing or culture duration (CD) and the parameters influencing the culture duration are some type of stirrer such as Rushton turbin, six inclined blades turbin, helix and anchor. The influence of other parameters were also studied such as the initial biomass concentration (So), the final desired biomass concentration (Xf), the velocity stirring (Vr), the initial biomass concentration (Xo) and the liquid density (mv).. The obtained results show how would be these parameters values in order to minimize the biomass culture duration.

Abstract: To promote coal purification, this study investigates the optimization and control of the downstream processes in coal gasification: the sour water gas shift reaction (SWGSR) and sour gas removal (AGR). For steady-state design, an SWGSR/AGR process model was developed using Aspen Plus software, and the simulation results were validated using data from the National Energy Laboratory. Three different process flow schemes were considered in the study. Based on the results of sensitivity analysis, the optimization variables for these processes included: steam injection flow rate, number of H₂S absorber trays, number of degassing tower trays, degassing tower feed stage, and degassing tower feed tray temperature. All of the above variables had a significant impact on the total annual cost (TAC) of each process flow scheme. The SWGSR/AGR process was designed to minimize TAC while maintaining product specifications. The TACs for Process Schemes 1, 2, and 3 were $98,967,790.9, $116,881,378.3, and $95,338,636.5, respectively. Regarding dynamic control, control structures for flow structures 1 and 3 (FS1 & FS3) are proposed. The automatic tuning method, detuning method, and Tyrens-Luyben tuning rule method are employed to determine controller parameters. Simulation results show that, under varying throughput and load disturbances, FS3 achieves faster disturbance rejection rates and setpoint tracking.

Abstract: Membrane-based processes offer promising sustainable alternatives for the decolourisation and purification of starch hydrolysates, yet membrane selection and operating conditions remain the most critical challenges. This study systematically evaluates the performance of polymeric ultrafiltration (UF) and nanofiltration (NF) membranes for starch hydrolysate syrup treatment. Experiments were conducted in a lab-scale cross-flow filtration system using five UF and three NF flat-sheet polymeric membranes under varying temperatures, transmembrane pressures, and feed concentrations. Separation performance was assessed through colour removal, sugar recovery, permeate flux, and alongside indicators of fouling behaviour. UF membranes with molecular weight cut-offs of 100, 70, and 5 kDa exhibited the most favourable performance at 60 °C and 8 bar, achieving partial colour removal (18–32 %) with high permeate fluxes (84–130 kg·m⁻²·h⁻¹) and limited sugar losses (0.7–19.9 %). NF membranes showed significantly higher colour rejection (32–100 %), but were associated with substantial sugar losses (up to 96 %), limiting their applicability for selective decolourisation; however, their high sugar retention capacity suggests potential for product concentration and the removal of low-molecular-weight impurities. Overall, UF represents a suitable approach for partial colour removal in starch hydrolysates, while NF may be better suited for product concentration and the removal of low-molecular-weight impurities, as well as auxiliary applications such as water recovery. These findings provide a systematic basis for membrane selection and process optimisation in industrial starch hydrolysate purification.

Abstract: Various methodologies have been developed over the years for assessing model predictive controller (MPC) performance. However, few are applied in industry, and they remain inefficient in providing a rapid indication of the causes of deteriorated control behavior. This article aims to review methodologies available in the literature addressing these challenges. Additionally, it proposes a structure in which the MPC performance assessment is carried out within a fault detection and diagnosis (FDD) framework. The integrated approach employs cascaded modules of machine learning (ML) binary classifiers arranged in a sequence that mimics the decision-making logic of an operator. To illustrate the integrated strategy both conceptually and operationally, the van de Vusse reactor, controlled by a nonlinear model predictive controller (NMPC), was adopted as a case study. The ML models used were Random Forest, Multilayer Perceptron, and Recurrent Neural Networks. The results showed that the models can correctly distinguish the cause of abnormality, even in the presence of noise in the measurements. Different ML models performed best for different diagnostic tasks, highlighting the flexibility of arranging models according to their most suitable application. The investigation indicated that the proposed ML-based FDD framework, which embeds control performance assessment, is competitive for control-aware diagnosis of MPC-controlled processes.

Abstract: This manuscript proposes to valorize the macauba endocarp (E) wastes from the macauba processing by a pyrolytic + hydrothermal process to produce activated carbon (ACE), followed by chemical treatment with H₂O₂ (A) and HNO₃ (B). Elemental analysis, surface area, pore-size distribution, and surface functional groups are investigated for the different prepared materials. ACEA and ACEB offer a higher surface area and functionality than Vulcan XC-72R (VC), making them suitable as carbon supports for Pd nanoparticles. The prepared Pd/ACEA and Pd/ACEB are physicochemically characterized by X-ray diffraction and Transmission Electron Microscopy. Their electrochemical performance is initially evaluated in a 3-electrode glass cell and is found to surpass that of Pd/VC in glycerol electro-oxidation. This trend is confirmed in the glycerol acid-alkaline electroreformer, where hydrogen and electricity were simultaneously produced, achieving a maximum power density of 0.28 kW m^-2 and H₂ flux of 0.6 STP m³ m^-2 h^-1 at 80 °C.

Abstract: Effective management of dairy manure is crucial for reducing environmental and public health risks. This waste material can serve as a viable source of bioenergy via anaerobic digestion. The recalcitrance of lignocellulosic fiber in manure presents challenges for its efficient conversion to methane. Hydrothermal pretreatment of manure fiber improves process performance by deconstructing the lignocellulosic structure. Low-temperature hydrothermal treatment of biomass optimally enhances the AD process performance by limiting the formation of inhibitory compounds such as furfurals. The integration of an optimal hydrothermal pretreatment within an anaerobic digestion system can improve the homogeneity, miscibility, and digestibility of dairy manure, thereby enhancing biogas yield. This review examines the hydrothermal treatment of lignocellulosic biomass, with a focus on dairy manure, the water chemistry involved in pretreatment, relevant process parameters, and the challenges faced in anaerobic digestion.

Abstract: This study presents the development of polylactide (PLA)-based biocomposites reinforced with 5, 10, and 15 wt.% polymethylsilsesquioxane (PMSQ) microparticles. The materials were prepared by melt blending followed by injection molding to evaluate the effect of PMSQ content on the morphological, thermal, mechanical, thermomechanical, rheological, and thermal conductivity properties of polylactic acid (PLA). Scanning electron microscopy (SEM) revealed a relatively uniform dispersion of PMSQ particles, especially at low filler content (up to 5 wt.%). Differential scanning calorimetry (DSC) showed that PMSQ incorporation did not significantly affect the melting temperature of PLA, while the cold crystallization temperature decreased by 14 °C combined with a higher crystallinity level. Thermogravimetric analysis (TGA) showed a slight improvement in thermal stability, with higher residual mass as PMSQ content increased. Mechanical tests showed a 7.4% reduction in Young's modulus and a 20% loss of tensile strength for the biocomposite containing 15 wt.% PMSQ due to limited interfacial stress transfer and particle agglomeration. However, dynamic mechanical analysis showed that low PMSQ concentration (5 wt.%) increased the storage modulus by 15% at 35 °C and slightly increased the glass transition temperature by up to 2 °C. Rheological analyses revealed higher storage modulus, loss modulus, and complex viscosity with increasing PMSQ content. Additionally, the presence of PMSQ enhanced the thermal conductivity of PLA by up to 23% at 75 °C. Overall, PLA/PMSQ biocomposites, especially at low PMSQ content, showed improved thermomechanical stability and thermal conductivity, suggesting their potential use in thermoforming applications.

Abstract: The electrochemical reduction of carbon dioxide (CO₂) to ethanol represents a promising pathway for sustainable fuel production and carbon utilization. However, the envi-ronmental performance of such systems is strongly dependent on electrolyzer configu-ration and electricity source. This study presents a comprehensive cradle-to-gate life cycle assessment (LCA) of CO₂-to-ethanol conversion using anion exchange membrane (AEM) and bipolar membrane (BPM) electrolyzer systems under different electricity scenarios, including grid, photovoltaic, wind, and waste-derived electricity. The life cycle inventory is developed using a combination of stoichiometric calculations, literature data, and en-gineering assumptions, with environmental impacts evaluated using the IPCC 2021 Global Warming Potential (GWP100) method. Results indicate that electricity consump-tion is the dominant contributor to environmental impacts, accounting for 70–90% of total emissions. Wind-based electricity scenarios exhibit the lowest impacts, with emissions as low as 0.32 kg CO₂-eq/kg ethanol for AEM systems, while grid-based BPM systems show the highest emissions, reaching up to 4.7 kg CO₂-eq/kg ethanol. Photovoltaic systems demonstrate intermediate performance due to embodied emissions from panel produc-tion. Across all scenarios, AEM systems consistently outperform BPM systems due to lower energy requirements. Monte Carlo analysis confirms the robustness of these findings, with limited overlap between best- and worst-performing scenarios. Overall, the results highlight the critical importance of low-carbon electricity and energy efficiency in im-proving the sustainability of CO₂ electroreduction systems and support their potential role in future carbon-neutral fuel production pathways.

Abstract: Grape bagasse is an abundant agro-industrial by-product and an important source of phenolic compounds with antioxidant properties. This study evaluated Soxhlet extrac-tion, supercritical fluid extraction (SFE), pressurized liquid extraction (PLE), subcriti-cal water extraction (SWE), and sequential extraction strategies for recovering bioac-tive compounds from grape bagasse. Box–Behnken designs were applied to SFE and PLE to evaluate process effects on extraction yield, while total phenolic content (TPC), total anthocyanin content (TAC), and antioxidant activity (ABTS) were additionally determined for PLE extracts. Hydroethanolic extractions showed greater selectivity toward phenolic compounds, whereas water-based extractions promoted higher yields associated with additional polar constituents. In SWE, increasing temperature en-hanced extraction yield and phenolic recovery, although anthocyanin contents de-creased under more severe thermal conditions. SWE provided higher extraction yields than PLE with comparable phenolic content and antioxidant activity, suggesting the recovery of additional highly polar non-phenolic compounds, whereas PLE resulted in higher extraction yields than SFE. Sequential extraction demonstrated that the first step accounted for most of the phenolic recovery and antioxidant activity, while the second aqueous step increased overall extraction yield. The sequential PLE–SWE route resulted in the highest TPC (198.0 mg GAE/g) and antioxidant activity (2321 μmol TE/g), demonstrating the potential of sequential extraction for grape bagasse fraction-ation and valorization.

Abstract: Electrochemical CO₂ reduction to ethanol is a promising route for circular-carbon fuel and chemical production, but practical implementation remains limited by coupled membrane, catalyst, transport and system-integration constraints. This Communication reassesses anion-exchange membranes (AEMs) and bipolar membranes (BPMs) using recent 2024–2026 literature. The central argument is that membrane selection is not a passive separation choice; it controls local pH, charge carriers, CO₂ availability, carbonate formation, water activity, proton/cation deliv-ery, product crossover and downstream techno-economic assessment (TEA) and life cycle assessment (LCA) burdens. AEM operation can create alkaline cathodic microenvironments that favor C–C coupling, but bicarbonate/carbonate formation imposes carbon-loss, salt-management and recovery penalties. BPM operation can improve pH separation and carbon management through water dissociation and bicarbonate acidification, but its viability depends on water-dissociation efficiency, co-ion exclusion, junction stability and voltage control. Recent ethanol-selective catalyst studies further show that copper oxidation state, grain boundaries, sub-surface dopants, ionomers, interfacial wettability and dynamic operation interact strongly with membrane-imposed microenvironments. The Communication pro-poses a membrane-centered decision framework linking AEM/BPM selection with ethanol selectivity, single-pass carbon utilization, energy efficiency, durability, TEA/LCA boundaries and future reactor design.

Abstract: The rapid advancement of Machine Learning (ML) has significantly transformed polymer science by enabling efficient prediction and design of polymer properties through high‑throughput screening. However, current methods still struggle with nonlinear Structure–Property Relationships (SPRs), limited dataset standardization, and computational inefficiency, which restrict prediction accuracy and interpretability. This study proposes a comprehensive ML‑based framework for predicting polymer properties and identifying SPRs. The approach integrates data preprocessing, molecular descriptor and topological index–based feature extraction, iterative feature selection, and XGBoost predictive modeling. Model hyperparameters are optimized using the Starfish Optimization Algorithm (SOA) to enhance performance and efficiency. Model interpretability is achieved through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing both global and local insights into the influence of molecular features on polymer properties. Experimental evaluation on the PolyOne dataset demonstrates strong predictive performance, with R² values exceeding 0.92, mean absolute error (MAE) below 0.08, and root mean square error (RMSE) under 0.12 for key physical and optical polymer properties. Overall, the proposed framework effectively balances accuracy, computational efficiency, and interpretability, offering a robust and practical tool for accelerating polymer design while enhancing understanding of molecular structure–property relationships.

Abstract: We conduct a theoretical analysis on the diffusiophoretic motion of a dielectric droplet in a cylindrical pore in the presence of an induced diffusion potential, such as in the NaCl electrolyte solution. The fundamental electrokinetic governing equations are solved using a patched pseudo-spectral method based on Chebyshev polynomials, coupled with a geometric mapping scheme to handle the irregular solution domain. The impact of boundary confinement effect on droplet mobility is examined in detail. Interesting electrokinetic phenomena are found in this work, such as mobility reversal in narrow cylindrical pores with the droplet moving against the direction expected based on the classical Coulomb electrostatic law due to the strong boundary confinement effect. Two critical points of κa are found, where κ is the electrolyte strength and a is the droplet radius. The spinning orientation on the droplet surface changes each time past them. The profound boundary confinement effect, both electrostatically and hydrodynamically, is responsible for these peculiar phenomena. The results presented here has direct applications in microfluidic and nanofluidic operations as well as drug delivery applications.

Abstract: The recycling of polyethylene terephthalate (PET) is gaining increasing importance, as it enables the conversion of plastic waste into valuable raw materials and contributes to a circular economy. Recent research has primarily focused on optimizing the depolymerization step of PET glycolysis, while downstream processes often overlooking the at least equally critical downstream steps in recovering the monomer bis(2-hydroxyethyl) terephthalate (BHET). The implementation of a water‑free PET glycolysis process eliminates challenges related to internal solvent and homogeneous catalyst recycling that commonly occur in conventional processes. This study therefore focuses on BHET crystallization and filtration as key downstream unit operations. Two nucleation strategies, gassing and seeding, were investigated and compared with experiments without a nucleation strategy. The aim was to achieve reproducible process control during crystallization and to obtain crystals with good filterability, which is essential for efficient washing and high product purity. Experiments without a nucleation strategy showed poor reproducibility. In contrast, gassing and seeding improved crystallization control, particularly regarding nucleation temperature and relative crystallization yield. However, these strategies also resulted in significantly prolonged filtration times due to differences in filter cake properties. The anisotropic crystals exhibited a broad particle size distribution with a high fraction of fine particles, leading to small and heterogeneous pores in the filter cake. Limited crystal growth was identified as the main cause of the unfavorable filtration behavior.

Abstract: Plastics pyrolysis is increasingly pursued as a pathway for producing circular hydrocarbon feedstocks for petrochemical integration. However, non-integrated reactor configurations often exhibit limited heat-transfer control, significant char handling requirements, and variable product distributions. This work presents a system-level interpretation of the MLM-R™ process, an integrated pyrolysis–combustion loop in which a circulating solid heat carrier enables continuous thermal supply through internal oxidation of carbonaceous residues. Material Flow Analysis (MFA) was applied to reconcile mass, elemental carbon, and chemical energy distributions across the defined process boundary. For the representative case study (1,000 kg polyolefin basis), ~81% of feed carbon and ~83% of feed chemical energy (HHV basis) were recovered in the condensed liquid product, while ~7% of feed carbon was internally combusted to sustain autothermal operation. Simulated distillation analysis indicates that removal of a ~15 wt% C34+ heavy fraction enables compliance with refinery-relevant boiling range targets (≥95% below 480°C). The combined MFA and physicochemical interpretation supports the role of integrated solids circulation and heat-transfer control as primary drivers of product selectivity and process scalability in circular feedstock production.