Search Results (589)

Residual microalgal biomass generated during wastewater phytoremediation represents an underexploited resource for developing sustainable bioprocesses. This study investigated the biotechnological valorization of phytoremediation-derived Chlorella biomass through an integrated process combining α-amylase-assisted saccharification and fermentation with the non-conventional yeast Wickerhamomyces sp. UFFS-CE-3.1.2 in a stirred-tank bioreactor. Following physical pretreatment to enhance intracellular compound accessibility, fermentation was conducted for 72 h under anaerobic conditions, and temporal changes in enzymatic activities and fermentation-associated compounds were monitored by spectrophotometric assays and high-performance liquid chromatography (HPLC), respectively. The integrated process exhibited distinct temporal profiles of hydrolytic and antioxidant enzyme activities, with maximum activities of 1275.23 U/mL for catalase, 1014.59 U/mL for ascorbate peroxidase, 1291.67 U/mL for protease, and 228.75 U/mL for lipase. Amylase activity remained detectable throughout the 72 h process. Total sugars decreased from 8.88 g/L at 0 h to 0.03 g/L at 72 h. In comparison, glycerol peaked at 5.62 g/L at 18 h, and ethanol remained at approximately 1.0 g/L between 18 and 48 h. Because several enzymatic activities were already detected before yeast inoculation, the observed profiles cannot be attributed exclusively to Wickerhamomyces sp. and should instead be interpreted as characteristics of the integrated bioprocess. Overall, the results demonstrate that residual Chlorella biomass generated during wastewater phytoremediation can serve as a renewable feedstock for further biotechnological processing, supporting an extended valorization pathway within a circular bioprocessing framework. Full article

This paper addresses fault diagnosis in nonlinear systems where reliable mathematical models are unavailable and only input–output measurements are accessible. The proposed methodology consists of three stages. First, a data-driven identification stage is performed using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to capture the nonlinear dynamics of the system from fault-free sensor data. This procedure yields a set of convex Takagi–Sugeno (TS) models representing the system dynamics. In the second stage, fault detection is achieved using zonotopic proportional–integral (PI) observers with convex structures. Robustness against parametric uncertainty and sensor noise is ensured through an ${\mathcal{H}}_{\infty }$ formulation expressed as a set of linear matrix inequalities (LMIs). Finally, fault isolation is carried out using a fault signature matrix (FSM). The zonotopic framework provides adaptive set-based residual bounds that act as adaptive thresholds for fault detection, while structured residual activation patterns enable reliable fault isolation. The proposed approach is evaluated on a continuous stirred tank reactor (CSTR) under sensor faults and incipient process faults in the presence of measurement noise and compared with representative data-driven methods. Results demonstrate improved diagnostic accuracy and reduced false-alarm rates while maintaining timely fault detection and reliable isolation. Full article

The global energy transition and rapid electrification are driving increased demand for copper. However, conventional pyrometallurgical and hydrometallurgical extraction routes are increasingly challenged by declining ore grades and stricter environmental regulations. Bioleaching involves the microbial catalysis of sulfide mineral dissolution and provides a sustainable method for copper recovery from low-grade ores, tailings and secondary resources. This review provides a critical and integrated analysis of copper sulfide bioleaching, covering microbial diversity, molecular mechanisms, mineralogical controls, operational parameters, and industrial applications. This review also examines the functional roles of prominent acidophiles, including the functional roles of prominent acidophiles, including Acidithiobacillus spp., Leptospirillum spp. and thermophilic archaea, in the oxidation of iron and sulfur, mitigation of passivation, and metal solubilization. The molecular underpinnings of these processes are explored by investigating iron and sulfur oxidation gene networks (the rus operon and sox cluster), copper resistance systems (CopA, CusCBA) and biofilm formation pathways. The mineralogical controls on the behavior of chalcopyrite (refractory/passivating), chalcocite (highly reactive) and bornite (intermediate) are critically assessed. The synergistic effects of key operational parameters (temperature, pH, redox potential, aeration and particle size) on leaching kinetics and microbial community dynamics are investigated. The scalability, efficiency and environmental footprint of industrial applications such as heap, dump, stirred-tank and in situ bioleaching are discussed. Despite more than four decades of commercial development, several challenges remain, such as slow chalcopyrite dissolution, passivation, metal toxicity, and scale-up limitations. Emerging solutions such as synthetic microbial consortia, multi-omics technologies, artificial intelligence-assisted optimization, and digital twins are identified as transformative approaches for next-generation biomining. In this review, microbiology, mineralogy, electrochemistry, and process engineering are integrated to demonstrate that biotechnological leaching is among the most promising technologies for the sustainable production of copper and to identify future directions for its industrial application. Full article

Acidogenic fermentation offers a promising approach to convert chicken manure into carbon-rich liquid products for various practical applications. However, the high nitrogen content in chicken manure hinders acidogenesis and limits product utilization due to ammonium accumulation. To address this, a 320-day fermentation experiment was conducted in a two-stage operation (Stage I: uncontrolled pH; Stage II: pH controlled at 5.0). pH adjustment was conducted by adding HCl. The continuously stirred tank fermentation reactor was operated at 37 °C and manually fed daily. Results showed that pH adjustment shifted the metabolic pathway from volatile fatty acid (VFA)-dominated production to lactic acid-enriched production: lactic acid increased from 12.6 to 35.7 g-COD/L, total VFAs decreased by 58%, while acetic acid remained at a relatively high level of chemical oxygen demand (COD) of 17.6 gCOD/L. Total ammonia nitrogen (TAN) decreased from 7.0 to 3.1 g/L, increasing the ratio of soluble COD (SCOD) to TAN from 19:1 to 38:1. The ammonium reduction was primarily due to selective inhibition of uric acid degradation under low pH, while protein hydrolysis remained largely unaffected. These findings demonstrate the potential of pH-regulated chicken manure fermentation liquor as a high-value product. Full article

Real-world fault data for hydrogen-cooled turbogenerators are scarce and largely proprietary, which hinders data-driven health assessment aligned with severity standards. This paper proposes a standards-aligned, multi-source ordinal fusion framework and demonstrates it, as a proof of concept, on a reproducible four-domain surrogate collection. The collection combines public industrial datasets—SKAB (cooling loop), UCI-WWT (water chemistry), CARE Wind Farm A (electrical and thermal conditions)—and a physics-informed hydrogen-side stream derived from Henry’s law and a continuously stirred tank reactor (CSTR) mass balance, joined by paired sampling. The collection is a methodological benchmark, not a validated diagnostic for any specific machine. A dual-head classifier supervised by a hybrid CORN + EMD ordinal loss, a multi-stream fusion backbone, and a calibrated ensemble with per-model temperature scaling are aligned with the four-level GB/T 43188-2023 scheme (Normal/Attention/Abnormal/Serious). All methods are evaluated under a unified protocol (mean ± standard deviation over three seeds; the deterministic calibrated ensemble is reported as a single value). On 600 fused test samples, the ensemble reaches F1-macro 0.5349, Accuracy 0.6717, Cohen’s κ = 0.4713, and quadratic-weighted kappa (QWK) 0.5948, improving F1-macro by +22.4 pp over the strongest full-scale single-source baseline (InceptionTime on CARE, trained under the identical protocol), with larger rank-aware gains (+30.4 pp on κ, +36.2 pp on QWK). It further improves by +8.9 pp over the strongest cross-entropy fusion baseline retrained under the identical protocol. The results support the methodological claim that fusing four heterogeneous monitoring domains under rank-aware ordinal supervision yields coherent, standards-aligned severity grades, offering a reproducible benchmark and methodology whose transfer to real hydrogen-cooled turbogenerators remains to be validated on co-recorded plant data. Full article

Biological methanation of carbon dioxide (CO₂) using hydrogen (H₂) offers a promising route for upgrading biogas and storing renewable energy within existing gas infrastructures. However, the low solubility of H₂ in aqueous systems remains a major bottleneck, limiting its bioavailability and overall conversion efficiency. In this study, three H₂ delivery strategies—direct bubbling, sparging, and diffusion through submerged silicone tubing—were systematically compared in a lab-scale continuous stirred-tank reactor (CSTR) to evaluate their relative mass-transfer performance. H₂ addition via silicone tubing yielded substantially higher initial H₂ mass-transfer rates than sparging and bubbling, with increases of approximately 3.8-fold and 5.3-fold, respectively, under the tested conditions. Based on these findings, silicone-based H₂ delivery was applied in both in situ and ex situ configurations for biomethanation. In both setups, the methane (CH₄) fraction in the biogas increased from approximately 48% to over 85% upon H₂ addition, while no H₂ accumulation in the reactor headspace was observed. Under the applied low organic loading conditions, biogas production remained stable, indicating that H₂ addition did not adversely affect process performance within the investigated operational window. Overall, this study provides a comparative assessment of H₂ delivery strategies and highlights the trade-off between mass-transfer performance and engineering feasibility. While silicone diffusion enhances H₂ availability in anaerobic systems at the laboratory scale, extrapolating from the experimentally determined H₂ flux suggests that an impractically large silicone surface area would be required for scale-up. This reflects a fundamental limitation of diffusion-based H₂ delivery in stirred-tank reactors and underscores the need for alternative reactor concepts, such as dedicated ex situ systems or high-surface-area gas–liquid contactors. Despite extensive research on hydrogenotrophic methanation, quantitative links between H₂ mass transfer, reactor design, and scalability remain insufficiently resolved. Full article

Biosurfactants are sustainable alternatives to petroleum-derived surfactants, yet their industrial application is often constrained by production costs and process efficiency. This study aimed to evaluate biosurfactant production by the psychrotolerant Antarctic bacterium Streptomyces luridus So3.2 using low-cost recycled frying oils under mild cultivation conditions and to validate the process in stirred-tank bioreactors. Cultivation conditions were optimized using response surface methodology (RSM), and the same optimized pH and carbon source concentration were subsequently applied in stirred-tank reactors at laboratory scales of 2 L and 20 L, while aeration (0, 0.5 and 0.9 vvm) and agitation speed (100, 150, and 200 rpm) were evaluated as reactor-operational variables. Filtered and centrifuged recycled frying oil yielded the highest biosurfactant performance (emulsification indices exceeding 80%, enhanced oil displacement, and surface tension values below 40 mN m⁻¹) compared to commercial oils. Biosurfactant production was growth-associated, with detectable surface activity within the first 24 h. RSM identified optimal cultivation parameters at pH 8.0, 3% (w/v) inoculum, and 2% (w/v) oil concentration. The biosurfactant exhibited a critical micelle concentration (CMC) of 29 mg L⁻¹ and a critical micelle dilution (CMD) of 43.2, yielding an estimated broth concentration of 1.25 g L⁻¹. At the 2 L reactor scale, moderate aeration (0.5 vvm) combined with intermediate agitation (150 rpm) preserved high surface activity, yielding emulsification indices above 83%, oil displacement halos of 12.5 cm, and surface tension values as low as 35.5 mN m⁻¹. This performance was also maintained during validation at 20 L. FTIR and TLC analyses indicated lipid- and peptide-associated functional groups. These findings were further complemented by HPLC, MALDI-TOF MS and genome-mining analyses (antiSMASH), revealing a complex molecular profile and multiple NRPS/NRPS-like biosynthetic gene clusters, supporting the interpretation of the recovered product as a putative lipopeptide-associated surface-active extract. Overall, this work demonstrates the feasibility of producing a biosurfactant-associated surface-active extract from recycled frying oils using S. luridus So3.2 under mild conditions. Full article

Denitrifying bioreactors are edge-of-field best management practices used to reduce excess nutrients in agricultural drainage. Traditional systems rely on woodchips as both a carbon source and microbial habitat, but woodchip-only bioreactors often exhibit limited nitrate removal, particularly under low temperatures and high flow conditions. This study evaluated nitrogen removal in a multimedia bioreactor under varying environmental conditions. A non-ideal, continuous stirred-tank model was applied to estimate nitrogen removal rates and nitrate removal efficiencies in mesoscale reactors containing walnut-shell biochar, Brotex, and woodchips. Two configurations—woodchip–biochar with and without Brotex—were tested at 4 h and 12 h hydraulic residence times (HRTs) and temperatures from 6 °C to 14.5 °C. Nitrate removal was consistently higher in the non-Brotex treatments. At low temperature, average removal was 15.3% (3.21 g m⁻³ d⁻¹) and 50.5% (3.66 g m⁻³ d⁻¹) for 4 h and 12 h HRTs, respectively. Removal improved at higher temperatures, reaching 54.2% (4.77 g m⁻³ d⁻¹) and 79.7% (7.86 g m⁻³ d⁻¹). These results exceeded many nitrate removal values reported for woodchip-only systems in the literature under similar temperature and hydraulic residence time conditions, indicating that alternative media can enhance performance across a range of flow and temperature conditions, supporting broader application in diverse climates. Full article

This work introduces a Targeted Emulsified-Solvent Injection (TESI) process for extracting bitumen from oil sands. In TESI, a solvent is emulsified near the emulsion phase inversion point (PIP), where the interfacial tension and the emulsion stability are very low. This allows the solvent to be easily emulsified and then deposited onto the bitumen-coated porous media (under lower shear conditions, where the emulsion breaks), mixing with bitumen, decreasing bitumen viscosity, and enabling mobilization and diluted bitumen recovery. The design of the surfactant-solvent formulation was guided by the Hydrophilic-Lipophilic-Difference and Net-Average-Curvature (HLD-NAC) frameworks. The HLD-NAC was used to identify a formulation with less than 1% surfactant exhibiting ultralow interfacial tension (~10⁻³ mJ/m²), at the PIP, where HLD = 0. This formulation was injected into columns packed with bitumen-coated sands at varying salinities and water-to-solvent ratios. Using optimal conditions, bitumen recoveries of up to 83% can be obtained at room temperature, without the need for steam or high-pressure injection, a condition suitable for intermediate-depth reservoirs. The effluent emulsion of diluted bitumen can be gravity-separated, allowing for the recycling of the aqueous solution containing the surfactant. The recovery curves were modeled using a continuous stirred tank reactor (CSTR) model coupled with a Capillary number model for thin viscous films that allowed the prediction of effluent diluted bitumen viscosities and an estimation of the pressure drops in the column that were consistent with experimental observations. Full article

Model predictive control (MPC) is a widely used advanced control strategy in industrial applications. The prediction horizon is one of its most influential tuning parameters, as it directly affects both control performance and computational demand. Despite its importance, systematic methods for its configuration remain scarce in the literature. This paper proposes a novel algorithm for prediction horizon estimation based on cross-correlation analysis of input and output data simulated by a trained long short-term memory (LSTM) network model of the controlled process. The use of LSTM networks allows the method to simulate process behaviour directly, eliminating the need for experiments on the physical system. Furthermore, this enables the method to work entirely offline, allowing the prediction horizon to be determined prior to deployment. The proposed algorithm is evaluated on two representative benchmark systems: a continuous stirred-tank reactor and a single tank system. LSTM models are trained for both benchmark systems and are subsequently integrated into an MPC framework. Closed-loop simulations demonstrate that MPC controllers designed with the estimated prediction horizons achieve strong control performance across both benchmark systems. The results suggest that cross-correlation analysis of LSTM-simulated data provides a reliable and systematic basis for prediction horizon estimation, contributing a practical tool for MPC tuning in industrial process control. Full article

Sensor-based process monitoring often involves both process variables and quality-related variables. These variables are usually used together to detect faults and to evaluate their effects on process quality or performance. However, most existing monitoring methods still rely on squared reconstruction residuals. This treatment assumes a fixed residual structure and may be insufficient for nonlinear industrial processes. In practice, residual variances may vary with operating conditions. Residuals from different variables may also be correlated. To address this problem, this paper proposes a probabilistic residual modeling method for process–quality fault detection. The method retains the latent-variable structure of deep variational canonical correlation analysis. It further introduces conditional residual distributions for the process side and the quality side. These distributions are parameterized by the latent operating state inferred from sensor measurements. Residual negative log-likelihoods are then used as monitoring statistics. In this way, residual abnormality is evaluated under the current operating condition. The proposed method is verified on a three-phase flow facility and a continuous stirred tank reactor process. Compared with PLS, CCA, DCCA, and DVCCA, the proposed method improves the detection of process-side disturbances and provides clearer separation between process-related and quality-related abnormal responses. Quantitatively, in the TPFF air line blockage case, the process-side statistic $J_x$ achieved an FDR of 82.02% with an FAR of 1.52%, compared with an FDR of 52.02% obtained by the corresponding DVCCA statistic $SP{E}_x$. In the TPFF open direct bypass case, $J_x$ and $J_y$ achieved FDRs of 90.87% and 91.07%, respectively, with FARs of 0.00%. In the CSTR coolant-temperature sensor-bias case, $J_x$ achieved an FDR of 88.29% with an FAR of 0.00%, while $J_y$ remained below its control limit, supporting process–quality fault discrimination. Full article

Industrial process fault diagnosis remains challenging because one-dimensional time-series data often involve complex dynamics, noise disturbances, and temporal dependencies, which hinder reliable fault representation and robust diagnostic decisions under complex operating conditions. To address these challenges, a fault diagnosis method for industrial processes based on the Multiscale Convolution-Attention-GRU Residual Network (MCAG-ResNet) is proposed. MCAG-ResNet integrates multiscale feature learning, attention-based feature recalibration, temporal dependency modeling, and residual learning in a unified architecture to enhance discriminative fault representation and diagnostic robustness. In addition, normalization and lightweight data augmentation are incorporated to improve training stability and generalization performance. Validation on the Tennessee Eastman (TE) and Continuous Stirred Tank Reactor (CSTR) datasets demonstrates the effectiveness, generalization capability, and diagnostic stability of the MCAG-ResNet in complex industrial process fault diagnosis. Further analyses, including variable contribution, feature importance, noise robustness, hyperparameter sensitivity, performance–complexity, and statistical stability analyses, verify its interpretability, robustness, parameter rationality, practical applicability, and stability. Full article

Bioreactor technology for medicinal plants provides a controllable platform for the conservation of rare and endangered resources, the production of high-value-added active ingredients, and green manufacturing of traditional Chinese medicine. Focusing on in vitro culture systems of medicinal plants, this article systematically reviews the application progress of stirred-tank, airlift, bubble column, wave-mixed, spray-type, temporary immersion, and photobioreactors in the culture of suspension cells, adventitious roots, hairy roots, shoots, and somatic embryos. Different from existing studies that mainly list reactor types, this review further provides a comprehensive analysis from the perspectives of physiological characteristics of the cultured objects, mass transfer and shear environment, medium and elicitor regulation, inoculation density, culture cycle, representative cases, and industrialization limitations. The results indicate that bioreactors can shorten production cycles, improve environmental controllability, and enhance product quality consistency; however, their large-scale application remains constrained by scale-up stability, metabolic fluctuations, downstream processing costs, GMP quality control, and commercial feasibility. Future research should shift from merely pursuing increased yield to integrated process development that is scalable, verifiable, low-cost, and quality-controllable. Full article

Anaerobic co-digestion (AcoD) is a promising strategy to enhance biogas production and improve the sustainability of wastewater treatment plants (WWTPs). However, information regarding process scale-up and reactor performance following the interruption of co-substrate feeding remains limited. This study evaluated the anaerobic co-digestion of municipal mixed sludge (MMS) and fruit and vegetable peel purées (FVPP) in a 10.6 L semi-continuously fed continuously stirred tank reactor (CSTR), operating under conditions representative of municipal WWTP anaerobic digesters. Mono-digestion (AMD) and co-digestion (AcoD) assays were conducted under mesophilic conditions and assessed through process performance indicators. AcoD increased methane concentration from 58.50% to 60.75%, while total volatile solids (TVS) removal efficiency increased from 41.67% to 59.84% in comparison with AMD. Total chemical oxygen demand (COD_T) removal efficiency also improved from 40.82% to 56.48%. Furthermore, H₂S concentrations decreased from approximately 350 ppmv during mono-digestion to 7 ppmv during co-digestion. An additional mono-digestion trial (aAMD) performed after co-substrate withdrawal achieved the highest specific methane production (0.27 L CH₄/g⁻¹ TVS) and organic matter removal efficiencies (63.73% for TVS and 67.55% for COD_T, respectively). These results demonstrate that co-digestion of MMS and FVPP improves methane quality, enhances organic matter removal, and reduces H₂S emissions, while maintaining stable reactor performance under scale-up conditions and after the interruption of co-substrate feeding. Full article

The electro-oxidation of persistent organic pollutants such as 2-chlorophenol (2-CPh) using boron-doped diamond (BDD) electrodes offers a promising wastewater treatment route, yet conventional mechanistic models (e.g., CFD) suffer from prohibitive computational costs. This study develops a hybrid physics-informed neural network (PINN) to model the electro-oxidation of 2-CPh in a flow-by reactor coupled with a continuous stirred tank under batch recirculation mode. The PINN integrates a diffusion–convection partial differential equation with a lumped-parameter ordinary differential equation for the tank, embedding physical constraints directly into the loss function. The model was trained on simulated data generated from a previously validated parametric model and optimized using a systematic hyperparameter grid search. The PINN achieved excellent agreement with experimental data, yielding a coefficient of determination (R²) of 0.9927, a mean square error of 0.0009, and a root mean square error of 0.0294—outperforming both the CFD and parametric models in accuracy. Sensitivity analysis revealed that the apparent kinetic constant is the most influential parameter (normalized sensitivity of 14.20). While the CFD model required 42 days and the parametric model 8 s, the PINN achieved a balanced trade-off with a runtime of 7.36 h. We conclude that the PINN provides a highly accurate, computationally feasible surrogate model suitable for integration into digital twins and real-time control frameworks for electrochemical wastewater treatment. Full article