Search Results (148)

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

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

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

This paper presents a mathematical modeling and advanced control strategy for the beer fermentation process, which is characterized by nonlinear biochemical kinetics and time-dependent dynamics. A biokinetic model was developed to describe the relationship between yeast growth, sugar consumption, and ethanol formation. The system was represented as a cascade of several continuous stirred-tank reactors (CSTRs), and experimental data confirmed a fermentation cycle of approximately 10 days. During this period, biomass concentration reached 6.8 g/L and ethanol levels exceeded 42 mmol/L. Substrate concentration (S) declined from 120 to 5 g/L, demonstrating effective conversion. The model was linearized around an operating point and reformulated into a 12-state-space system with input variables: temperature (set at 20–22 °C) and pH (maintained within 4.2–4.5). These inputs were controlled using fuzzy logic control (FLC) and model predictive control (MPC). Simulation results indicated that the FLC reduced temperature deviation to ±0.3 °C and minimized pH fluctuation below ±0.05. The MPC strategy improved substrate consumption efficiency by 8.5% and decreased fermentation time by 12 h under optimized input profiles. The combined FLC–MPC scheme demonstrated superior robustness, smooth trajectory tracking, and adaptability to biological variability compared to traditional methods. The developed framework supports intelligent brewery automation and provides a scalable foundation for further integration of digital fermentation technologies. Full article

The temperature regulation of nonlinear continuous stirred tank reactor (CSTR) processes remains a challenging control problem due to strong nonlinearities, time-delay effects, and sensitivity to disturbances and parameter variations. Conventional proportional–integral–derivative (PID)-based control strategies often fail to provide the robustness and precision required under such conditions, motivating the use of more flexible controller structures and advanced optimization techniques. In this study, an enhanced joint-opposition artificial lemming algorithm (JOS-ALA) is proposed for the optimal tuning of a fractional-order PID (FOPID) controller applied to CSTR temperature control. The proposed JOS-ALA incorporates a joint opposite selection mechanism into the original ALA to improve population diversity, convergence stability, and resistance to local optima stagnation. A nonlinear CSTR model is linearized around a stable operating point, and the resulting model is employed for controller design and optimization. The FOPID controller parameters are tuned by minimizing a composite cost function that simultaneously accounts for tracking accuracy, overshoot suppression, and instantaneous error behavior. The effectiveness of the proposed approach is assessed through extensive simulation studies and benchmarked against state-of-the-art and high-performance metaheuristic optimizers, including ALA, electric eel foraging optimization (EEFO), linear population size reduction success-history based adaptive differential evolution (L-SHADE), and the improved artificial electric field algorithm (iAEFA). The benchmarking set is further extended with the success rate-based adaptive differential evolution variant (L-SRTDE) to broaden the comparative evaluation. Simulation results demonstrate that the JOS-ALA-based FOPID controller consistently achieves superior performance across multiple criteria. Specifically, it attains the lowest mean cost function value of 0.1959, eliminates overshoot, and yields a normalized steady-state error of 4.7290 × 10⁻⁴. In addition, faster transient response and improved robustness under external disturbances and measurement noise are observed when compared with competing methods. Statistical reliability of the observed performance differences is additionally examined using a Wilcoxon signed-rank test conducted over 25 independent runs. The resulting p-values confirm that the improvements achieved by the proposed approach are statistically significant at the 5% level across all pairwise algorithm comparisons. These findings indicate that the proposed JOS-ALA provides an effective and reliable optimization framework for high-precision temperature control in nonlinear CSTR systems and offers strong potential for broader application in complex process control problems. Full article

Reliable multivariable control is critical for industrial sectors where processes exhibit severe nonlinearities and interactions. A Continuous Stirred Tank Reactor (CSTR) is a rigorous benchmark for testing control strategies addressing these complexities. This work first establishes a linear MIMO mathematical framework to define the specific structure of such interactive systems. Analysis via phase planes and steady-state analysis reveals low controllability, bistability, and strong coupling, leading to the collapse of traditional decoupled control schemes. To address these issues via multivariable control, we propose a centralized MIMO RST control structure synthesized via a Matrix Fraction Description (MFD) and the extended Bézout equation. Simulations for performance evaluation and comparison highlight the following key findings: (1) the centralized RST maintains stability and tracking precision in regions where decentralized RST loops fail; (2) it exhibits performance comparable to the Augmented State Pole Placement with Integral Action (ASPPIA) method and outperforms the standard Model-Based Predictive Control (MPC) baseline, particularly during critical equilibrium point transitions; and (3) it offers a robust yet computationally simple design that provides superior flexibility for pole placement, accommodating future identification-based models and adaptive tuning. These results validate our algebraic synthesis as a robust, computationally efficient solution for managing highly interactive nonlinear dynamics. Full article

The optimization of volatile fatty acid (VFA) production from complex wastes under anaerobic conditions remains constrained in terms of productivity by the common use of long hydraulic retention times (HRTs, 20–30 days). Extended HRTs can limit process productivity by reducing substrate turnover and reactor throughput, while promoting further conversion of VFAs into methane and other end products. Despite its importance, the combined influence of pH and HRT on VFA yields and process optimization has not been comprehensively evaluated. This study investigates the effects of pH and short HRT on VFA production, microbial community structure, and hydrolysis and acidification efficiency in continuous stirred-tank reactors (CSTRs) fed with carbohydrate-rich feedstock (carrot residue pulp). Operating at an HRT of 11 days and an organic loading rate (OLR) of 4.4 g COD·L⁻¹·d⁻¹ at 25 °C under pH 5.1 resulted in a VFA bioconversion efficiency of ~45% and an acidification efficiency of 84%, without compromising VFA profile or productivity compared to reactors operated at 14 days HRT and 3.3 g COD·L⁻¹·d⁻¹. The shorter HRT and higher OLR enhanced hydrolysis efficiency (60%) and promoted greater microbial diversity, supporting robust hydrolytic activity and stable production dominated by acetic and butyric acids. These findings challenge the conventional assumption that longer retention times inherently improve process stability and demonstrate that operational conditions might improve reactor space–time yield in VFA-oriented fermentations. Full article

Anaerobic digestion (AD) of poultry manure often faces ammonia inhibition due to its high nitrogen content. This study investigated a combined strategy involving mild thermal hydrolysis pretreatment and bioaugmentation with ammonia-acclimatised inoculum to enhance methane production and process stability under ammonia-stressed conditions. Batch biomethanation efficiency assays were first conducted to evaluate the effect of different hydrolysis conditions (55–70 °C, 30–60 min) on substrate methane yields and biodegradability. The optimal condition (70 °C for 60 min) increased methane potential by 8.7% compared to the untreated substrate. In addition, a mesophilic continuous stirred-tank reactor (CSTR) experiment was conducted using both non-hydrolysed and thermally hydrolysed poultry manure under hydraulic retention times of 25 and 30 days, across four operational phases: steady-state, ammonia toxicity, bioaugmentation recovery, and increased organic loading rate. CSTRs were subjected to ammonia stress (6500 mg NH₄⁺-N L⁻¹) to assess the effectiveness of an acclimatised bioaugmentation inoculum. Methane yields recovered up to 93% and 100% of pre-inhibition and ammonia-toxicity levels, respectively, accompanied by process stability while reaching 7280 mg NH₄⁺-N L⁻¹. The synergistic application of hydrolysis and bioaugmentation significantly improved substrate conversion and overall AD robustness. This integrated approach provides a viable and scalable strategy for optimising AD performance of nitrogen-rich feedstocks, enabling its future application in AD plants. Full article

A modified Enzyme Action Optimizer (mEAO) is proposed to tune a Fractional-Order Proportional–Integral–Derivative (FOPID) controller for precise temperature regulation of a nonlinear continuous stirred tank reactor (CSTR). The nonlinear reactor model, adopted from a standard benchmark formulation widely used in CSTR control studies, is employed as the simulation reference. The tuning framework operates in a simulation-based manner, as the optimizer relies solely on the time-domain responses to evaluate a composite cost function combining overshoot, settling time, rise time, and steady-state error. Comparative simulations involving EAO, Starfish Optimization Algorithm (SFOA), Success History-based Adaptive Differential Evolution with Linear population size reduction (L-SHADE), and Particle Swarm Optimization (PSO) demonstrate that the proposed mEAO achieves the lowest cost value, the fastest convergence, and superior transient performance. Further comparisons with classical tuning methods, Rovira 2DOF-PID, Ziegler–Nichols PID, and Cohen–Coon PI, confirm improved tracking accuracy and smoother actuator behavior. Robustness analyses under varying set-points, feed-temperature disturbances, and measurement noise confirm stable temperature regulation without retuning. These findings demonstrate that the mEAO-based FOPID controller provides an efficient and reliable optimization framework for a nonlinear thermal-process control, with strong potential for future real-time and multi-reactor applications. Full article

Anaerobic digestion of sugarcane vinasse is integral to enhancing ethanol distilleries’ environmental and energy performance by converting organic waste into biogas; however, the flammable and toxic nature of biogas has led to significant safety concerns, particularly in anaerobic bioreactors where biogas is produced and stored. This study provides a comparative risk assessment of different anaerobic reactor configurations—a covered lagoon biodigester (CLB), a continuous stirred-tank reactor (CSTR), an upflow anaerobic sludge blanket reactor (UASB), and an anaerobic structured-bed reactor (AnSTBR)—processing vinasse, focusing on fire, explosion, and hydrogen sulfide (H₂S) toxicity hazards. Jet fire scenarios posed the most severe threat, with fatal outcomes extending up to 66 m, while the fireball scenario exhibited no lethal range. The risks to human life from explosions were minimal (1.2%). H₂S toxicity was identified as the most critical consequence, with particularly severe impacts in CLB systems, where the hazardous zone was up to 20 times larger than in AnSTBR. Therefore, the design of anaerobic bioreactors for vinasse treatment must primarily address the risks associated with H₂S-rich biogas, as reactor configuration plays a key role in mitigating or amplifying these hazards—high-rate systems such as AnSTBR and UASB demonstrating safer profiles due to their compact design and lower gas storage volumes. Full article