Stirred Tank Reactor Research Articles

Energy recovery from poultry manure in the process of semi-continuous anaerobic co-digestion with sewage sludge

In the face of significant legislative changes in renewable energy sources and conservation of natural resources, bio-waste must be managed efficiently. Developing wastewater treatment technologies generate a large amount of sewage sludge requiring appropriate use. A well-known and practiced process is the anaerobic digestion of sewage sludge, but due to its characteristics, the production of biogas is not sufficient enough to ensure the energy self-sufficiency of a wastewater treatment plant. Another waste whose management is a challenge is poultry manure, whose production in Poland is high due to intensive poultry farming. This study investigated the possibility of co-digestion of poultry manure and sewage sludge and its effect on increasing biogas production compared to processing sludge alone. The process was performed in two continuously stirred-tank glass reactors at mesophilic conditions, with 20 days of hydraulic retention time. In the first stage, the batch assay with sewage sludge was applied to promote the development of an anaerobic community. The second stage involved feeding the reactors on a semi-continuous regime with sewage sludge for the control sample and a mixture with the gradual addition of poultry manure from 2.5 % to 60 %. During the experiment, the most important parameters affecting the process and the quantity and quality of the obtained products in the form of biogas and digestate were monitored. The study’s results indicated an advantage of the co-digestion process of poultry manure and sewage sludge over the digestion of sludge alone in terms of process efficiency. The highest biogas production was obtained with a co-substrate ratio of 42 % manure to 58 % sewage sludge (ratio based on volatile solids). Co-digestion had no significant effect on gas quality; in both cases, the methane content was more than 60 %. Moreover, a digestate with fertilizer potential was obtained for each sample.

Valorization of process water from hydrothermal carbonization of food waste by dark fermentation

Hydrothermal carbonization (HTC) of food waste produces hydrochar—a suitable biofuel—and process water (PW), which has a high organic content suitable for material and energy recovery. In this work, we study the effect of pH (4.8, 5.3, and 6.0) and organic loading rate (OLR; 2.5, 5.0, and 7.5 gCOD L−1 d−1) throughout the dark fermentation (DF) of PW from the HTC of food waste (180 °C, 1 h) in a continuous stirred tank reactor. The highest hydrogen yield (197.5 mL H2 L−1 d−1) was reached at pH 4.8 and OLR 5 gCOD L−1 d−1, associated with a prevalence of Clostridium bacteria. The highest volatile fatty acids concentration (10.2 gCOD·L−1) was achieved at pH 4.8 and OLR 7.5 gCOD L−1 d−1, with a dominance of Actinobacteria phylum. The integrated system HTC-DF allowed a potential energy recovery of 11.2 MJ kg−1.

MIMO system identification and uncertainty calibration with a limited amount of data using transfer learning

Multiple-input multiple-output (MIMO) systems are fundamental in numerous advanced engineering applications, from aerospace to telecommunications, where precise system identification is critical for optimal performance. However, the identification of such systems often faces significant hurdles due to data scarcity, with existing approaches typically requiring substantial amounts of data for effective training. Addressing this challenge, this paper introduces a novel transfer learning framework designed specifically for MIMO system identification under conditions of limited data and inherent uncertainties. The proposed framework is applied to two case studies: the first in metal additive manufacturing, specifically the laser-blown powder-directed energy deposition as the source domain and the laser hot wire-directed energy deposition as the target domain, and the second involving a nonlinear case study of a continuous stirred-tank reactor (CSTR) with a temperature-dependent reaction. The results underscore the framework's effectiveness in capturing the dynamics of the target systems, including the ability to effectively model nonlinear dynamics. Comparative analyses highlight the benefits of employing dimensionless numbers in dynamic system modelling, offering reduced dimensionality, more physical meaning, and increased model accuracy. Overall, the proposed framework presents a promising approach to enhance system identification in MIMO systems with limited data and uncertainties, with potential applications across diverse domains.

Nonideal mixing effect based on Cholette’s model on Van de Vusse reaction selectivity dynamics in an isothermal continuous stirred-tank reactor

Abstract The nonideal mixing effect on the Van de Vusse reaction dynamic in a continuous stirred-tank reactor (CSTR) has rarely been studied in literature. This work presents the mixing operation condition and kinetic reaction constant effects on the Van de Vusse reaction selectivity dynamic in an isothermal CSTR based on Cholette’s model. The mixing condition effect is defined using the parameter μ = n/m, where n is the flow fraction to the reactor and m is the dead space fraction in the reactor. In Cholette’s model, μ = 1 and μ ≠ 1 represent, respectively, the ideal and nonideal mixing operations. Herein, the final desired product yield steady state in the phase plane is first constructed under reaction constant and mixing condition combinations, indicating that the larger the μ values, the higher the desired product steady-state yield. In the selectivity dynamic simulations, the phase plane is divided into transient extreme yield region and region of monotonic variation in yield. It is found for the first time that a higher μ value leads to a decrease in the transient maximum selectivity region area and an increase in the transient minimum selectivity region area in the phase plane. Moreover, the trajectory from the start up to the final steady state will be changed due to nonideal mixing. These results have not been published and have direct applications in real reactor design and operation. Since a CSTR with ideal mixing is hardly attainable in practice, our results benefit the real reactor design and startup policy development in the desired product transient maximum yield.

Impact of hydromechanical stress on CHO cells’ metabolism and productivity: Insights from shake flask cultivations with online monitoring of the respiration activity

The hydromechanical stress is a relevant parameter for mammalian cell cultivations, especially regarding scale-up processes. It describes the mechanical forces exerted on cells in a bioreactor. The maximum local energy dissipation rate is a suitable parameter to characterize hydromechanical stress. In literature, different studies deal with the effects of hydromechanical stress on CHO cells in stirred tank reactors. However, they often focus on lethal effects. Furthermore, systematic examinations in smaller scales like shake flasks are missing. Thus, this study systematically considers the influence of hydromechanical stress on CHO DP12 cells in shake flask cultivations. By utilizing online monitoring of the oxygen transfer rate, the study simplifies and enhances the resolution of examinations. Results indicate that while lethal effects are absent, numerous sub-lethal effects emerge with increasing hydromechanical stress: The process time is prolonged. The time of glucose and glutamine depletion, and the lactate switch correlate positively linear with the logarithmic average energy dissipation rate while the maximum specific growth rate correlates negatively. Strikingly, the final antibody concentration only declines at the highest tested average energy dissipation rate of 3.84Wkg-1 (only tested condition with a turbulent flow regime and therefore a higher maximal local energy dissipation rate) from about 250mgL-1 to about 180mgL-1. This study presents a straightforward method to examine the impact of hydromechanical stress in shake flasks, easily applicable to any other suspension cell line. Additionally, it offers valuable insights for scale-up processes, for example into stirred tank reactors.

Physics-informed neural networks for dynamic process operations with limited physical knowledge and data

In chemical engineering, process data are expensive to acquire, and complex phenomena are difficult to fully model. We explore the use of physics-informed neural networks (PINNs) for modeling dynamic processes with incomplete mechanistic semi-explicit differential–algebraic equation systems and scarce process data. In particular, we focus on estimating states for which neither direct observational data nor constitutive equations are available. We propose an easy-to-apply heuristic to assess whether estimation of such states may be possible. As numerical examples, we consider a continuously stirred tank reactor and a liquid-liquid separator. We find that PINNs can infer immeasurable states with reasonable accuracy, even if respective constitutive equations are unknown. We thus show that PINNs are capable of modeling processes when relatively few experimental data and only partially known mechanistic descriptions are available, and conclude that they constitute a promising avenue that warrants further investigation.

Application of continuous stirring tank reactor for controllable synthesis of Cu7S4 nanocrystals

As a typical model for a chemical reactor in industry, the continuous stirring tank reactor has been widely used in waste-water treatment, industrial catalysis, and biological fermentation as well as great application prospects in the synthesis of nanomaterials. In this report, a convenient lab-scale continuous stirring tank reactor was assembled and utilized to synthesize Cu7S4 nanocrystals by injecting cupric bromide and sulfur solution in the presence of ascorbic acid and polyethyleneimine at 60 °C. The morphology control of Cu7S4 nanocrystals was accomplished by simply adjusting the agitation speed. Cu7S4 nanofibers with an average diameter of 48 ± 4.42 nm and a length of several micrometers were obtained at 80 rpm. Cu7S4 nanoplates with a thickness of 89 ± 13.56 nm and a plane size of 103 ± 16.47 nm were synthesized at 90 rpm, and the size of the nanoplates was regulated by continuously increasing the agitation speed from 90 rpm to 1000 rpm. Furthermore, the influences of two additional conditions (the mean residence time and the concentration of the feed solution) on the morphology of Cu7S4 nanocrystals, were also investigated. Therefore, these results could facilitate the understanding of the behaviors of CSTR in the synthesis of Cu7S4 nanocrystals and could also provide an important reference for the continuous synthesis of other nanoparticles.

CFD modeling of CO2 fixation by microalgae cultivated in a lab scale photobioreactor

The green microalga Chlorella vulgaris has been studied for efficient fixation of carbon dioxide, CO2, from elevated concentrations of in-feed gas flows. In this work, a Computational Fluid Dynamics (CFD) model for algae cultivation including the dependence on CO2 concentration in liquid has been derived based on cultivation experiments of Chlorella vulgaris. The experiments were performed in a laboratory scale cylindrical stirred tank reactor with a range of enriched CO2 concentrations in-feed (0.04–50 %). The model provided by Béchet et al. (2015) for algae cultivation considering dependence on local light, algae concentration and temperature has been implemented for CFD and modified for comprising CO2 dependence. The model has been verified with experimental results for the algae productivity showing the proper trends for dissolved CO2 concentrations and algae concentrations. Thus, the developed CFD model can serve as a tool for evaluating photobioreactor performance for CO2 capture

Advanced Control Strategies for Continuous Stirred Tank Reactors:

This paper investigates the control of a Continuous Stirred Tank Reactor (CSTR), with a primary emphasis on achieving temperature stability and optimizing reactant conversion. The CSTR poses challenges due to its nonlinear and exothermic behavior. To address these challenges, Model Predictive Control (MPC) is employed, a powerful strategy for handling complex systems. Additionally, Particle Swarm Optimization (PSO) is introduced to fine-tune MPC parameters, ensuring optimal performance. By integrating PSO with MPC, this study enhances control capabilities, specifically targeting the intricate demands of CSTR systems.

Trajectory-based breakup modelling for dense bubbly flows

A new model to predict the breakup of gaseous bubbles in a continuous liquid phase is developed. In the model each bubble is modelled as a spring–damper system, namely a Kelvin–Voigt element, while the outer force is derived by a Lagrangian analysis determining the largest stretching rate of the flow field below. The developed model is based on physical principles and no further arbitrary parameters have to be adjusted. Each bubble is observed on its way through the bubbly flow individually, taking into account its history along its respective path. With the implemented model numerical simulations in a wide range of scales are conducted, ranging from the laboratory scale of a vessel of 3L to the large industrial scale of 15m3. The simplicity of the model allows for a good cost to benefit ratio. In the present work, the achieved results are compared to experimental data obtained from optical measurements in a replica of a 200L aerated stirred tank reactor for various stirrer frequencies.

Enhanced Glycerolysis of Fatty Acid Methyl Ester by Static Mixer Reactor.

This study investigates the synthesis of monoglycerides (MGs) and diglycerides (DGs) from glycerol (G) and fatty acid methyl ester (FAME) using a static mixer reactor (SMR), which combines a static mixer (SM) with a reactor tank. The SMR integrates Kenics static mixers (KSM) and low-pressure drop static mixers (LPDSM) with varying length-to-diameter ratios (L/D = 1.0 and 1.5). Keys glycerolysis parameters, including the G:FAME molar ratio of 2:1-3:1, 2-3 wt % potassium hydroxide (KOH), and reaction time of 30-90 min at 150 °C were systematically explored. The SMR design allows precise control over the reaction time without altering the feed flow rate or tube length and avoiding agitator leakage. The optimal operating conditions, determined through a face-centered central composite design, resulted in 71.35% MGs and 14.20% DGs at a 3:1 molar ratio of G to FAME, 3 wt % KOH, 60 min, and 150 °C using an LPDSM with an L/D of 1.5. In comparison, an LPDSM with an L/D of 1 achieved 79.28% MGs and 10.17% DGs under the same conditions. When applied to purified crude glycerol, these conditions yielded 61.09% MGs and 23.44% DGs. The study found that a lower L/D ratio improved the mixing efficiency but increased the pressure drop. The SMR demonstrated superior performance in glycerolysis compared with conventional stirred tank reactors and ultrasonic probe reactors, indicating its potential for enhanced industrial application.

A Computer Vision Approach toward Verifying CFD Models of Stirred Tank Reactors.

Mixing is one of the most important nonchemical considerations in the design of scalable processes. While noninvasive imaging approaches to deliver a quantifiable understanding of mixing dynamics are well-known, the use of imaging to verify computational fluid dynamics (CFD) models remains in its infancy. Herein, we use colorimetric reactions and our kinetic imaging software, Kineticolor, to explore (i) the correlation of imaging kinetics with pH probe measurements, (ii) feed point sensitivity for Villermaux-Dushman-type competing parallel reactions, and (iii) the use of experimental imaging kinetic data to qualitatively assess CFD models. We report further evidence that the influences of the stirring rate, baffle presence, and feed position on mixing in a tank reactor can be informatively captured with a camcorder and help experimentally verify CFD models. Overall, this work advances scarce little precedent in demonstrating the use of computer vision to verify CFD models of fluid flow in tank reactors.

Fast pyrolysis of paper sludge in a continuous stirred-tank reactor and liquid-liquid extraction of benzenoid aromatics from fast pyrolysis bio-liquid

Paper sludge is a solid waste in paper mills and is conventionally treated by, e.g., landfill, composting, and incineration. Paper sludge contains paper production fillers and lignocellulosic biomass, both of which can be recycled to recover circular minerals and produce bio-based fuels and chemicals by, e.g., thermochemical recycling technology namely fast pyrolysis, for circular bioeconomy. In this paper, three different paper sludge samples collected in The Netherlands and Spain were analyzed by thermogravimetric analysis, moisture analysis, ash analysis, CHNS elemental analysis, powder X-ray diffraction, and X-ray fluorescence spectrometry. Fast pyrolysis of paper sludge was carried out in a lab-scale continuous stirred tank reactor at 500 ± 10 °C with a paper sludge feeding rate of 1 kg h−1. The recovery of circular minerals, which are mainly calcium carbonate, is 87.1 ± 2.2 % (on mineral basis). The yields of fast pyrolysis bio-liquid and biochar are 49.2 ± 6.7 wt% (on biomass basis, equivalent to 23.7 ± 2.2 wt% on paper sludge basis) and 23.8 ± 8.3 wt% (on biomass basis). Fast pyrolysis bio-liquid is a diluted aqueous containing various oxygenates (major, including alcohols, acids, benzenoid aromatics, aldehydes, ketones, ethers, and esters) and hydrocarbons. Liquid-liquid extraction of the fast pyrolysis bio-liquid using CH3OH/H2O and SO2/H2O was further performed to obtain an improved bio-liquid with relatively high concentration of the desired bio-based chemicals (namely benzenoid aromatics with the concentration of 53.1–65.9 area%). Both SO2/H2O and CH3OH/H2O show high liquid-liquid extraction efficiency to concentrate the benzenoid aromatics for 3.4–11.0 times. This work shows the fast pyrolysis followed by liquid-liquid extraction for the valorization of paper sludge, of which the former has been recently demonstrated on a pilot-scale unit in industry. However, the latter still needs to be further developed by, e.g., focusing on the extraction solvent and continuous liquid-liquid extraction process integrated to fast pyrolysis.

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. The method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.

System identification of a nonlinear continuously stirred tank reactor using fractional neural network

Chemical processes are vital in various industries but are often complex and nonlinear, making accurate modeling essential. Traditional linear approaches struggle with dynamic behaviour and changing conditions. This paper explores the advantages of the new theory of fractional neural networks (FNNs), focusing on applying fractional activation functions for continuous stirred tank reactor (CSTR) modeling. The proposed approach offers promising solutions for real-time modeling of a CSTR. Various numerical analyses demonstrate the robustness of FNNs in handling data reduction, achieving better generalization, and sensitivity to noise, which is crucial for real-world applications. The identification process is more generalized and can enhance adaptability and improve industrial plant management efficiency. This research contributes to the growing field of real-time modeling, highlighting its potential to address the complexities in chemical processes.

Continuous Flow Microreactors for the High-efficiency Enzymatic Synthesis of 10-Hydroxystearic Acid from Oleic Acid.

Converting fatty acids into specialty chemicals is sustainable but hindered by the low efficiency and thermal instability of current oleic acid hydratases, along with mass transfer limitations in emulsion reactions. This study introduces an optimized continuous flow micro-reactor (CFMR) that efficiently transforms oleic acid at low (15 g L-1) and high (50 g L-1) concentrations, improving reaction efficiency and overcoming key conversion barriers. The first CFMR model showed reaction speeds surpassing traditional batch stirred tank reactors (BSTR). Optimizations were performed on three key components: liquid storage, mixer, and reaction section of the CFMR, with each round's best conditions carried into the next. This achieved a space-time yield of 597 g L-1 d-1 at a 15 g L-1 oleic acid load. To further enhance the yield, we optimized the emulsifier system to solve incomplete emulsification and developed a two-component feed microreactor (TCFMR) that addressed mass transfer limitations caused by the product at high substrate loads, reaching a 91 % conversion of 50 g L-1 oleic acid in 30 minutes, with a space-time yield of 2312 g L-1 d-1. These advancements represent significant progress in utilizing fatty acids and advancing sustainable chemical synthesis.

Rate expression model from thermodynamics application and optimal kinetic parameters determination for urea synthesis and production process

Urea, an essential organic fertilizer, enhances soil fertility by providing 0.466 nitrogen for maximum crop yield. In this study, urea is synthesized from NH3 and CO2 in an equilibrium reaction process adhering to Le Chatelier's principle, maintained under process conditions: flow rate of 63.5 kg/s, temperature of 184 °C, and pressure of 160 kg/cm2. A new rate expression model, formulated in terms of extent of reaction and mole fraction, was developed based on mass action relations and thermodynamic models. Two industrial reactors were considered: a plug flow reactor (PFR) at Notore and a continuous stirred tank reactor (CSTR) at Indorama plants. Transient reactor models, based on material and energy balance conservation principles, were numerically resolved using MATLAB version 2020 with specified input conditions. A non-linear regression statistical optimization model was employed to refine kinetic parameter values, ensuring optimal and high-quality urea yield. Model validations were conducted using literature data, revealing higher urea yields of 0.726 and 0.7032 for the CSTR and PFR, respectively. Deviations (0.134, 0.10 to 1.135 and 0.635, 0.326 to 0.850) and root mean square errors (RMSE) (0.043, 0.033 to 0.193 and 0.137, 0.087 to 0.162) were observed when validated against plant and literature values for the CSTR and PFR respectively. The refined kinetic parameters (activation energies, Arrhenius constants, and rate constants) exhibited negligible deviations (0.0004–0.0466 and 0.0004 to 0.0491) and RMSE (0.0228, 0.0055, and 0.0256 and 0.0241, 0.0096, and 0.0269) when validated against plant data, significantly enhancing urea yield in CSTR and PFR reactors respectively.

Impact of random packing on residence time distribution of particles in bubbling fluidized beds: Part 1–cross-current flow reactors

In this work, the influence of employing random packings on the residence time distribution in a bubbling fluidized bed is investigated. The bubbling fluidized bed cold-flow reactor setup allows for continuous cross-current flow of particles. Expanded clay aggregate (ECA) is employed in the packed-fluidized bed experiments as the packing. The effects of different parameters such as packing type (ECA or no packing), Fluidization number (4.4, 6.6, and 8.8), and solid throughflow rates (92, 133, and 164 g/s) are investigated. The axial dispersion and tank-in-series models are used to categorize flow patterns of particles in the packed-fluidized beds and compared to beds using no packing. Results show that the vessel’s dispersion number for solids decreases in the presence of ECA packings up to fourfold compared to unpacked beds. Furthermore, tank-in-series model shows that the number of tanks for experiments utilizing packing increases by up to threefold compared to unpacked beds. The experimental results are also compared to a model known as a hybrid model. The hybrid model considers a continuous-stirred-tank-reactor in series with a plug-flow-reactor. Comparison of the model to the measured data shows a clear shift of the relative size or residence time from the stirred tank reactor towards the plug flow reactor with axial dispersion in the packed-fluidized bed compared to a bed with no packing. Also the vessel dispersion number of the plug flow reactor model with axial dispersion is significantly decreased in the case of packed-fluidized bed.

Novel stationary basket reactor for effective biomass delignification with deep eutectic solvent

Biomass pretreatment and conversion are crucial for sustainable development, but lack information on equipment that ensures effective mass transfer and easy biomass separation post-process. This work introduces a novel basket reactor with a stationary bed (StatBioChem) for biomass processing using deep eutectic solvents (DESs). We compared the delignification efficiencies of soft and hard biomass samples processed in the StatBioChem reactor, a stirred tank reactor (STR), and a commercial SpinChem® reactor. The StatBioChem design allowed DES to flow evenly through biomass in the basket, achieving the highest delignification degree, particularly for hard biomass. This effect was not observed in the SpinChem® basket reactor. High delignification led to increased glucose yields in subsequent enzymatic hydrolysis. The StatBioChem effectively combines the simplicity and efficiency of an STR with the ease of solvent recovery typical of basket reactors.

Clostridium autoethanogenum alters cofactor synthesis, redox metabolism, and lysine-acetylation in response to elevated H2:CO feedstock ratios for enhancing carbon capture efficiency

BackgroundClostridium autoethanogenum is an acetogenic bacterium that autotrophically converts carbon monoxide (CO) and carbon dioxide (CO2) gases into bioproducts and fuels via the Wood–Ljungdahl pathway (WLP). To facilitate overall carbon capture efficiency, the reaction stoichiometry requires supplementation of hydrogen at an increased ratio of H2:CO to maximize CO2 utilization; however, the molecular details and thus the ability to understand the mechanism of this supplementation are largely unknown.ResultsIn order to elucidate the microbial physiology and fermentation where at least 75% of the carbon in ethanol comes from CO2, we established controlled chemostats that facilitated a novel and high (11:1) H2:CO uptake ratio. We compared and contrasted proteomic and metabolomics profiles to replicate continuous stirred tank reactors (CSTRs) at the same growth rate from a lower (5:1) H2:CO condition where ~ 50% of the carbon in ethanol is derived from CO2. Our hypothesis was that major changes would be observed in the hydrogenases and/or redox-related proteins and the WLP to compensate for the elevated hydrogen feed gas. Our analyses did reveal protein abundance differences between the two conditions largely related to reduction–oxidation (redox) pathways and cofactor biosynthesis, but the changes were more minor than we would have expected. While the Wood–Ljungdahl pathway proteins remained consistent across the conditions, other post-translational regulatory processes, such as lysine-acetylation, were observed and appeared to be more important for fine-tuning this carbon metabolism pathway. Metabolomic analyses showed that the increase in H2:CO ratio drives the organism to higher carbon dioxide utilization resulting in lower carbon storages and accumulated fatty acid metabolite levels.ConclusionsThis research delves into the intricate dynamics of carbon fixation in C. autoethanogenum, examining the influence of highly elevated H2:CO ratios on metabolic processes and product outcomes. The study underscores the significance of optimizing gas feed composition for enhanced industrial efficiency, shedding light on potential mechanisms, such as post-translational modifications (PTMs), to fine-tune enzymatic activities and improve desired product yields.