Search Results (111)

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

A robust multi-sensor recursive Expectation-Maximization (RMSREM) algorithm is proposed in this paper for autoregressive eXogenous (ARX) models, addressing the challenges of heavy-tailed noise, as well as the difficulty in simultaneously processing multi-sensor information. First, for the potential outliers in industrial processes, the Student’s t-distribution is introduced to model the statistical characteristics of measurement noise, whose heavy-tailed property enhances the algorithm’s robustness. Second, a recursive framework is integrated into the Expectation-Maximization (EM) algorithm to satisfy the real-time requirement of dynamic system identification. Through a recursive scheme of the Q-function and sufficient statistics, model parameters are updated in real-time, allowing them to adapt to time-varying system characteristics. Finally, by exploiting the redundancy and complementarity of multi-sensor data, a multi-sensor information fusion mechanism is designed that adaptively calculates the weight of each sensor based on the noise variances. This mechanism effectively fuses multi-source observation information and mitigates the impact of single-sensor failure or inaccuracy on identification performance. Numerical examples and simulations of the continuous stirred-tank reactor (CSTR) demonstrate the validity of the proposed RMSREM algorithm. Full article

System security is a very important organizational task for the system to maintain proper functioning and to prevent modifications or hijacking of the system. Indeed, it is necessary to address any detected problem or defect to protect human beings, industry, and machines. So the identification, after the fault detection phase, of the variables correlated to the detected or occurred fault is a very important step. For this purpose, this paper proposes a nonlinear machine learning method for fault diagnosis. Indeed, the Reduced Kernel Partial Least Squares (RKPLS) is proposed as a processing method for the suitable localization of detected faults. The idea of this approach is to generate partial RKPLS models, using the principle of structured symmetry residues, with reduced sets of variables. On the other hand, the Fault Isolation (FI) using the online RKPLS method (ORKPLS) is developed in this article to generate indices of fault detection sensitive to certain faults and insensitive to others. Thus, a partial ORKPLS method, for fault isolation, is proposed to secure the systems and ensure a proper operation. The suggested approaches are applied for monitoring the continuous stirred tank reactor (CSTR) and the Air quality monitoring network (AIRLOR). The obtained results underscore the role of leveraging symmetry in designing fault. Full article

The hydrodynamic and reaction characteristics of poly-alpha-olefin (PAO) polymerization in a continuous stirred tank reactor (CSTR) under Eulerian–Eulerian multiphase flow and a finite-rate chemical kinetics model were studied in this paper. A mathematical framework correlating 1-decene conversion with operational and structural parameters was established. Numerical simulations revealed an axial circulation flow pattern driven by combined impellers, with internal coils enhancing heat exchange and flow guidance. The gaseous catalyst, injected below the turbine impeller, achieved rapid dispersion and low gas holdup. The results demonstrated that 1-decene conversion exhibited insensitivity to impeller speed under fully turbulent mixing (mixing time <0.15% of space time), suggesting limited mass transfer benefits from further speed increases. Conversion positively correlated with temperature and space time, albeit with diminishing returns at prolonged durations. Series reactor configurations improved conversion efficiency, though incremental gains decreased with additional units. Optimal reactor design should balance conversion targets with economic factors, including energy consumption and capital investment. These findings provide critical insights into scaling PAO polymerization processes, emphasizing the interplay between reactor geometry, mixing dynamics, and reaction kinetics for industrial applications. Full article

Anaerobic digestion (AD) is a mature industrial fermentation technology for converting organic matter into renewable bioenergy, and chicken manure (CM) is a promising feedstock due to its high organic content. However, the industrial-scale AD of CM is often hindered by ammonium inhibition, particularly under high organic loading rates (OLRs). Biochar has emerged as a sustainable additive that can enhance microbial activity, buffer pH, and improve system stability. In this study, the effects of biochar on the methane production and fermentation performance of CM in terms of AD were evaluated under both batch and continuous conditions, where batch experiments were conducted at different biochar-to-CM ratios. Ammonium nitrogen and methane production were monitored to determine the optimal biochar addition ratio. Continuous stirred-tank reactors (CSTRs) were then operated with the optimal biochar addition ratio under stepwise-increasing OLR conditions to assess methane production, fermentation parameters, and methanogen community composition. The results showed that an optimal biochar addition of 9% reduced total ammonium nitrogen (TAN) by 31.75% and increased cumulative methane production by 25.93% compared with the control. In continuous operation, biochar addition mitigated ammonium inhibition, stabilized pH, enhanced system stability and organic loading capacity, and improved methane production by 21.15%, 27.78%, and 83.33% at OLRs of 2.37, 4.74, and 7.11 g volatile solids (VS)/(L·d), respectively, compared to the control. Biochar also inhibited the growth of methylotrophic methanogen of RumEn_M2. These findings provide scientific and technical support for applying biochar as a process enhancer during the AD of CM. Full article

This paper addresses the challenge of trajectory tracking in non-minimum-phase systems, which are known for their limitations in performance and stability within process control. The primary objective is to evaluate the feasibility of using linear-algebra-based control strategies to achieve precise tracking in such systems. The primary hypothesis is that internal model-based compensators can transform non-minimum-phase behavior into equivalent minimum-phase dynamics, thereby enabling the application of linear algebra techniques for controller design. To validate this approach, both simulation and experimental tests are conducted, first with a Continuous Stirred Tank Reactor (CSTR) model and then with the TCLab educational platform. The results show that the proposed method effectively achieves robust trajectory tracking, even in the presence of external disturbances and sensor noise. The primary contribution of this work is to demonstrate that internal model-based compensation enables the application of linear control methods to a class of systems that are typically considered challenging to control. This not only simplifies the design process but also enhances control performance, highlighting the practical relevance and applicability of the approach for real-world non-minimum-phase systems processes. Full article

The depletion of fossil fuel reserves and their environmental impact have driven the search for sustainable energy alternatives. Biodiesel has emerged as a promising substitute. Being a major byproduct of the coffee industry, spent coffee grounds (SCGs) offer a viable feedstock due to their abundance, high fatty acid content, and calorific value. This study explores biodiesel production from SCGs. First, oil was experimentally extracted from SCGs using Soxhlet extraction with hexane as the solvent. The oil yield varied between 12 and 13.4% with a density of 0.9 g/mL. Reactor modeling and kinetic analysis were performed, showing that CSTRs in series are favorable for the esterification and transesterification reactions. Furthermore, Aspen Plus was used to simulate the extracted oil conversion into biodiesel through a two-step esterification and purification process. The simulation results are verified against previous experimental research. Sensitivity analyses were performed to evaluate the influence of key process parameters, including methanol-to-oil ratio, reactor residence time, and transesterification temperature. The simulation results indicate an optimal biodiesel mass yield of 90.31%, with a purity of 99.63 wt%, at a methanol-to-oil ratio of 12:1 and a transesterification temperature of 60 °C. Full article

Extremophilic biological process (EBP) pretreatment increases substrate availability in anaerobic digestion, but the effect on downstream microbial community composition in industrial systems is not characterized. Changes in microbial communities were determined at an industrial facility processing dairy manure in a modified split-stream system with three reactor types: (1) EBP tanks at 70–72 °C; (2) mesophilic Continuously Stirred Tank Reactors (CSTRs); (3) mesophilic Induced Bed Reactors (IBRs) receiving combined CSTR and EBP effluent. All reactors had a two-day hydraulic retention time. Samples were collected weekly for 60 days. pH, volatile fatty acid and bicarbonate concentrations, COD, and methane yield were measured to assess tank environmental conditions. Microbial community compositions were obtained via 16S rRNA gene sequencing. EBP pretreatment increased acetate availability but led to a decline in the relative abundance of acetoclastic Methanosarcina species in downstream IBRs. Rather, syntrophic methanogens, e.g., members of Methanobacteriaceae, increased in relative abundance and became central to microbial co-occurrence networks, particularly in association with hydrogen-producing bacteria. Network analysis also demonstrated that these syntrophic relationships were tightly coordinated in pretreated digestate but absent in the untreated CSTRs. By promoting syntrophic methanogenesis while increasing acetate concentrations, EBP pretreatment requires system configurations that enable acetoclast retention to prevent acetate underutilization and maximize methane yields. Full article

A multiphase model of hydrothermal liquefaction (HTL) using the computational fluid dynamics coupling discrete element method (CFD-DEM) is used to simulate biocrude oil production from sludge flocs in a continuous stirred tank reactor (CSTR). Additionally, the influence of the agitator speed and the slurry flow rate on dynamic biocrude oil production is investigated through full transient CFD analysis in a scaled-up CSTR study. The kinetics of the HTL mechanism as a function of temperature, pressure, and residence time distribution were employed in the model through a user-defined function (UDF). The multiphysics simulation of the HTL process in a stirred tank reactor using the Lagrangian–Eulerian (LE) approach, along with a standard k-ε turbulence model, integrated HTL kinetics. The simulation accounts for particle–fluid interactions by coupling CFD-derived hydrodynamic fields with discrete particle motion, enabling prediction of individual particle trajectories based on drag, buoyancy, and interphase momentum exchange. The three-phase flow using a compressible non-ideal gas model and multiphase interaction as design requirements increased process efficiency in high-pressure and high-temperature model conditions. Full article

With the rising automation and complexity level of industrial systems, the efficiency and accuracy of fault diagnosis have become a critical challenge. The convolutional neural network (CNN) has shown some success in the fault diagnosis field. However, typical convolutional kernels are commonly fixed-sized, which makes it difficult to capture multi-scale features simultaneously. Additionally, the use of numerous fixed-size convolutional filters often results in redundant parameters. During the feature extraction process, the CNN often struggles to take inter-channel dependencies and spatial location information into consideration. There are also limitations in extracting various time-scale features. To address these issues, a fault diagnosis method on the basis of a dual-path mixed convolutional attention-BiGRU network (DMCA-BiGRUN) is proposed for industrial processes. Firstly, a dual-path mixed CNN (DMCNN) is designed to capture features at multiple scales while effectively reducing the parameter count. Secondly, a coordinate attention mechanism (CAM) is designed to help the network to concentrate on main features more effectively during feature extraction by combining the channel relationship and position information. Finally, a bidirectional gated recurrent unit (BiGRU) is introduced to process sequences in both directions, which can effectively learn the long-range temporal dependencies of sequence data. To verify the fault diagnosis performance of the proposed method, simulation experiments are implemented on the Tennessee Eastman (TE) and Continuous Stirred Tank Reactor (CSTR) datasets. Some deep learning methods are compared in the experiments, and the results confirm the feasibility and superiority of DMCA-BiGRUN. Full article

This work investigates, for the first time, the application of a modified natural magnetite material with 35% of lanthanum for phosphorus (P) recovery from synthetic and actual wastewater under both static (batch) and dynamic (continuous stirred tank reactor (CSTR)) conditions. The characterization results showed that the natural feedstock mainly comprises magnetite and kaolinite. Moreover, the lanthanum-modified magnetite (La-MM) exhibited more enhanced textural, structural, and surface chemistry properties than the natural feedstock. In particular, its surface area (82.7 m² g⁻¹) and total pore volume (0.160 cm³ g⁻¹) were higher by 86.6% and 255.5%, respectively. The La-MM efficiently recovered P in batch mode under diverse experimental settings with an adsorption capacity of 50.7 mg g⁻¹, which is significantly greater than that of various engineered materials. It also maintained high efficiency even when used for the treatment of actual wastewater, with an adsorption capacity of 47.3 mg g⁻¹. In CSTR mode, the amount of P recovered from synthetic solutions and real wastewater decreased to 33.8 and 10.2 mg g⁻¹, respectively, due to the limited contact time. The phosphorus recovery process involves mainly electrostatic attraction over a wide pH interval, complexation, and precipitation as lanthanum phosphates. This investigation indicates that lanthanum-modified natural feedstocks from magnetite deposits can be regarded as promising materials for P recovery from aqueous solutions. Full article

Temperature control in a continuous stirred tank reactor (CSTR) poses significant challenges due to the process’s inherent nonlinearities and uncertain parameters. This study proposes an innovative solution by developing an adaptive nonlinear proportional–integral–derivative (NPID) controller. The nonlinear gain that dynamically scales the error fed to the integrator is enhanced for optimized performance. The network’s ability to approximate nonlinear functions and its online learning capabilities are leveraged by effectively integrating an NPID control scheme with a radial basis function neural network (RBFNN). This synergistic approach provides a more robust and reliable control strategy for CSTRs. To assess the proposed method’s feasibility, a set of simulations was conducted for tracking, disturbance rejection, and parameter variations. These results were compared with those of an adaptive RBFNN-based PID (APID) controller under identical conditions. The simulations indicated that the proposed method achieved reductions in maximum overshoot of 33.7% and settling time of 54.2% for upward and downward setpoint changes and 27.2% and 5.3% for downward and upward setpoint changes compared to the APID controller. For disturbance changes, the proposed method reduced the peak magnitude (M_peak) by 4.9%, recovery time (t_rcy) by 23.6%, and integral absolute error by 16.2%. Similarly, for parameter changes, the reductions were 3.0% (M_peak), 26.4% (t_rcy), and 24.4% (IAE). Full article

This laboratory study investigated the anaerobic co-digestion process of the halophyte S. ramosissima (Sram) together with swine manure (SM) in different mixing ratios in batch and continuous reactor experiments. In the batch experiments, a methane yield of 214 mL_CH4·gVS⁻¹ was obtained for Sram in mono-digestion. In co-digestion with SM, the methane yields were slightly higher than calculated from the yields of each substrate in mono-digestion. Also, the kinetic rate constant in the co-digestion with swine manure increased from 0.219 d⁻¹ for mono-digested S. ramosissima to 0.318 d⁻¹ in the co-digestion of 50:50 Sram:SM (based on VS). Two continuous 5 L lab-scale CSTR reactors were operated: one as a control (100% SM) and the other as a co-digestion reactor with an increasing VS share of Sram (15, 25, and 35%) in the feed. Both reactors were operated at an organic loading rate (OLR) of 2.5 gVS.L⁻¹·d⁻¹ and a hydraulic retention time (HRT) of 20 days. In the continuous process, the highest methane yield of 276 mL_CH4·gVS⁻¹ was achieved at a co-digestion VS ratio of Sram:SM 25:75, corresponding to a methane yield from the added S. ramosissima of 277 mL_CH4·gVS⁻¹. This showed successful operation of the continuous co-digestion process of S. ramosissima and swine manure, with higher methane yields of S. ramosissima than in the mono-digestion batch tests. Full article