Search Results (373)

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

In this study, a sustainable calcium-rich biochar was synthesized via co-pyrolysis at 800 °C of marble waste, animal manure, and lignocellulosic biomass. This biochar (MWM–B) was comprehensively characterized and then valorized for phosphorus (P) removal from real effluent and synthetic solutions in both batch and continuous stirred tank reactor (CSTR) modes. Characterization results confirm the formation and deposition of significant amounts of calcium oxides and calcium hydroxides on the biochar surface, which enhance the biochar’s surface chemistry and textural properties. In batch mode, MWM–B efficiently removes P with a removal capacity (108.4 mg g⁻¹) that is 5.3 times higher than that observed in the CSTR system. This efficiency drop is due to the limited contact time between phosphate species and the biochar particles. Interestingly, the presence of calcium and magnesium in the continuously renewed real effluent in the CSTR system increases P removal efficiency by approximately 136% compared with synthetic solutions. A detailed analysis of MWM–B before and after P removal suggests that this process occurs mainly through precipitation as hydroxyapatite, complexation with hydroxyl functional groups, electrostatic interactions, and hydrogen bonding. This work confirms that MWM–B generated at 800 °C is an attractive material for P removal from effluents. Full article

In 2024, China generated approximately 130 million tons of food waste. This study focuses on food wastewater characterized by exceptionally high organic strength (chemical oxygen demand (COD) > 80 g·L⁻¹, total suspended solids (TSS) > 20 g·L⁻¹) content. Conventional continuous stirred tank reactors (CSTRs) inherently couple hydraulic retention time (HRT) and sludge retention time (SRT), making them prone to microbial washout under high organic loading. To overcome this limitation, this study employed two anaerobic sequencing batch reactors (ASBRs) for treating such high-strength food wastewater. This study systematically evaluated the impacts of temperature (mesophilic: 37 °C and thermophilic: 55 °C) and organic loading rate (OLR) on fermentation performance. Under stable operation (OLR = 5.6 kg_COD·m⁻³·d⁻¹; HRT = 16 days), the mesophilic ASBR achieved a specific methane yield of 307 mL CH₄·g_CODremoved⁻¹, an average COD removal efficiency of 81%, and a volatile fatty acids-to-total alkalinity (VFA/TA) ratio of 0.2, indicating robust process stability. In contrast, the thermophilic ASBR exhibited a VFA/TA ratio of 0.5, signaling incipient acidification. Microbial community analysis revealed significantly higher bacterial and archaeal alpha diversity in the mesophilic system. Notably, Methanothrix—a versatile acetoclastic methanogen—dominated the mesophilic archaeal community (66.65%), conferring functional redundancy and resilience against organic shock loads. By contrast, the thermophilic system was overwhelmingly dominated by the hydrogenotrophic Methanothermobacter (99.28%), resulting in low functional diversity and structural fragility. Compared with a benchmark mesophilic CSTR (specific methane yield: 276 mL CH₄·g_CODremoved⁻¹; COD removal efficiency: 70.6%), the mesophilic ASBR improved methane yield by 11%, COD removal efficiency by 15%, and operational stability (VFA/TA = 0.2 vs. 0.6). This work addresses a gap in ASBR applications for high-strength food wastewater treatment and provides experimental validation of the performance, stability, and scalability of mesophilic ASBRs. The proposed process represents a technically feasible, resource-efficient, and operationally robust solution for the valorization of organic wastewater with COD > 80 g·L⁻¹ and TSS > 20 g·L⁻¹. Full article

Anaerobic digestion is a key technology for chicken manure valorization, but ammonia accumulation often causes system instability. In this study, a 100-day continuous stirred tank reactor (CSTR) experiment was conducted under mesophilic conditions to investigate the mechanisms of ammonia inhibition in chicken manure at total solids (TS) contents of 8% (T1), 12% (T2), and 16% (T3). Compared to T1, the peak TAN concentrations in T2 and T3 were 64.28% and 73.82% higher. After 100 days, pH in T2 and T3 dropped by 5.19% and 7.65% relative to T1. Volatile fatty acid (VFA) accumulation increased by 4.6- and 6.5-fold, while the TS-based methane yield decreased by 52.94% and 73.11%, respectively. Metagenomic analysis revealed the mechanisms of ammonia inhibition: high-ammonia conditions not only directly suppressed the gene abundance of methanogenic pathways but also systematically reduced the abundance of hydrolytic bacteria and acidogenic fermentative bacteria, leading to a disruption in the supply chain of methanogenic precursors, while ammonia-tolerant microbiota became competitively enriched. This study elucidates the multi-level mechanism of ammonia inhibition in high-TS chicken manure digestion at the functional gene level, providing a theoretical basis for the precise regulation of ammonia stress and improvement of system stability. Full article

Microaeration has been applied to enhance anaerobic digestion (AD), although the underlying mechanisms remain unclear. This work proposes that improving methanogenic activity can be achieved by alleviating micronutrient deficiencies and enhancing digestibility. The microaeration technique was employed to enhance the methanogenic activity of cassava wastewater (CW) both with and without added cassava residue (CR) and to improve CR digestibility in a continuous stirred tank reactor (CSTR) at 37 °C. The sole CW had the optimal COD loading rate of 1.71 kg/m³d. The addition of CR at 1000 mg/L to the CW resulted in the greatest methanogenic improvement of 88% compared with the sole CW and provided the greatest digestibility of CR. Under the optimal specific O₂ dosage rate (3 mL/LRd), the improvements in CH₄ yields were 251% and 140% in comparison to those of the sole CW and the CW with added CR, respectively. Additionally, it achieved substantial improvements in digestibility for the cellulose (59%), hemicellulose (61%), and remaining starch (67%) fractions of added CR. However, lignin degradation remained unaffected, a potential area for future optimization. This work opens new avenues for enhancing biogas production from wastewater by adding agricultural residue in conjunction with microaeration. Full article

Nonlinear model predictive control (NMPC) delivers high tracking accuracy for industrial processes but requires solving a nonlinear program at each sampling instant, limiting its applicability under tight real-time constraints. The Koopman operator provides a principled route to circumvent this limitation by embedding nonlinear dynamics into a higher-dimensional space where the evolution becomes linear, thereby reducing the online optimization to a convex quadratic program. This paper presents a Koopman-based MPC framework (K-MPC) that incorporates three algorithmic contributions. First, an adaptive radial basis function dictionary learning procedure selects lifting functions from process data, eliminating manual basis selection and improving approximation fidelity for systems with localized nonlinearities. Second, a recursive least-squares update rule adjusts the Koopman matrix online as new measurements arrive, enabling the controller to track slow parameter drifts without full model recomputation. Third, a tube-based constraint tightening strategy accounts for the residual linearization error, preserving recursive feasibility under bounded Koopman approximation mismatch. Simulations on a Van der Pol oscillator, a continuous stirred-tank reactor (CSTR), and a four-state Tennessee Eastman-inspired distillation column demonstrate that K-MPC achieves root-mean-square tracking errors within 11–16% of NMPC while reducing average per-step computation time by a factor of 14 to 18. The recursive update mechanism reduces prediction error by 80% compared to the fixed offline Koopman model when reactor feed concentration drifts by 15% from its nominal value. Ablation experiments confirm that adaptive dictionary learning and online updating each contribute measurably to closed-loop performance. Full article

The aim of this study was to design and validate an automated 5 L prototype Stirred-Tank Photobioreactor (ST-PBR) dedicated to the two-stage cultivation of the microalga Haematococcus pluvialis. The classic limitations of stirred-tank reactors (such as high shear stress and suboptimal light penetration) were overcome through precise phase-controlled illumination (60 and 300 μmol m⁻² s⁻¹) and the implementation of an advanced embedded control system integrated with Keysight VEE Pro 9.33 software. The design features an innovative mixing system utilizing a dual marine impeller driven by a brushless motor—operating at a mathematically defined tip speed of 0.48 m/s to preserve cellular integrity—alongside a precise gas dosing strategy (pH-stat) employing medical-grade components. Process verification demonstrated highly stable operation, maintaining a dry biomass concentration of 1.315 g/L with no recorded sedimentation, while achieving a highly competitive astaxanthin biosynthesis yield of 4.12% dry weight (DW). Furthermore, enzymatic extraction facilitated the recovery of a product with high biological activity, as confirmed by an increase in equine adipocyte viability up to 128.1 ± 3.1% in in vitro MTS assays, highlighting its potential for veterinary nutraceutical applications. The developed solution represents a scalable, cost-effective, and viable alternative to advanced tubular photobioreactors. Full article

Biological reactions are widely applied in processes such as bioenergy production, raw material manufacturing, and resource recovery from waste. As a main reactor type, the stirred-tank bioreactor exhibits prominent advantages of high mixing efficiency and strong adaptability. At present, the optimization of bioreactors mainly focuses on rigid impellers, and the research on flexible impellers is insufficient. Identifying the influence of flexible materials on bioreactor performance is of great significance. In this work, a stirred-tank bioreactor equipped with flexible blades was designed. In addition, a performance detection method coupling Particle Image Velocimetry (PIV) and image recognition was proposed to systematically study the effects of stirring speed, liquid environment, and impeller type. The results indicated that compared with rigid impellers, flexible impellers could reduce 7.7% low-velocity zones and save 15% mixing time. Velocity could be distributed more uniformly, and the suitable velocity ratio was increased by 7.88%. Moreover, the power consumption had been reduced by 7.49%. Taking into account the mixing efficiency and the impact of shear stress, the optimized structural combination and operating parameters were a pitched blade turbine (PBT)-propeller impeller type and a stirring speed of 300 rpm. This work provides important references for the design and optimization of stirred-tank bioreactors. Full article

Industrial production of single-cell oils (SCOs) by oleaginous yeasts relies predominantly on nitrogen limitation, which constrains process flexibility when nitrogen-rich substrates are used. Although phosphate limitation has been reported as an alternative lipid induction strategy, its process-level performance relative to nitrogen limitation remains insufficiently resolved under controlled reactor-scale conditions. In this study, phosphate-limited, nitrogen-limited and nutrient-replete cultivations of Cutaneotrichosporon oleaginosum ATCC 20509, Saitozyma podzolica DSM 27192, Scheffersomyces segobiensis DSM 27193 and Apiotrichum porosum DSM 27194 were benchmarked in 2.5 L stirred-tank reactors operated under identical media compositions and process parameters. Biomass formation, lipid titres, specific lipid production rates, biomass composition and fatty acid profiles were systematically compared. Nitrogen limitation resulted in the highest lipid titres, reaching up to 9.2 g L⁻¹ (A. porosum), while maximum lipid titres under phosphate-limited conditions reached 5.0 g L⁻¹ (C. oleaginosum) and nutrient-replete conditions 3.9 g L⁻¹ (A. porosum), respectively. The highest specific lipid production rate under nitrogen limitation was 0.0028 g gCDW⁻¹ h⁻¹ (S. podzolica), while phosphate limitation yielded a maximum of 0.0037 g gCDW⁻¹ h⁻¹ (S. podzolica). These results demonstrate that phosphate limitation can decouple cellular lipid productivity from biomass formation and represents a process-relevant alternative for SCO production from nitrogen-rich feedstocks. Full article

The growing demand for functional foods reflects greater consumer awareness of diet–health links, with bioactive peptides receiving increasing attention for their health-promoting effects. In this study, bioactive peptides exhibiting antioxidant, dipeptidyl peptidase-IV (DPP-IV) inhibitory, and angiotensin-converting enzyme (ACE) inhibitory activities were produced from a jack bean (Canavalia ensiformis) protein isolate using a continuous proteolysis system with two enzymes. This study encompassed two major phases: isolating protein from jack beans and implementing a continuous enzymatic hydrolysis process. Key variables examined included the enzyme-to-substrate ratio ([E]/[S]), pH level, and residence time (τ). Optimal performance was achieved at [E]/[S] = 5%, pH = 7.5, and τ = 12 h, yielding a permeate with peptide content of 0.6143 mg SE/mL, along with notable antioxidant capacity and ACE inhibition of 0.0454 mg TEAC/mL and 92.18%, respectively. These results confirm that the jack bean protein isolate is a viable substrate for generating multifunctional bioactive peptides. This study provides a foundation for scalable and sustainable production of functional food ingredients from underutilized legumes using continuous bioprocessing technology. Industrial relevance: Integrating a stirred tank reactor with membrane separation provides a promising approach for continuous bioactive peptide production using a free-enzyme system, helping to streamline processing, reduces the demand for enzyme immobilization, and minimizes batch-to-batch variability. This study shows that continuous hydrolysis of jack bean protein isolate in EMR can enhance antioxidant activity and ACE inhibition of the hydrolysates. This approach offers a safer and more efficient route to support the commercialization of jack bean-based functional products. Full article