Batch Process Research Articles

Continuous Microreactor‐Based Synthesis of SnO2 Nanoparticles for Sensor Applications

Tin oxide nanoparticles are well‐established materials with a wide range of applications, including optoelectronic devices and solid‐state gas sensors. Conventional synthesis methods of these systems are often based on batch processes. In this study, we compare batch and continuous synthesis methods for tin dioxide nanoparticles using precipitation and sol‐gel processes. For the continuous processes we applied the so called microjet reactor method. The nanoparticles were characterized by TEM, elemental analysis and XRD and exhibited particle sizes of 1.7‐3.0 nm and crystallite sizes of 1.7‐2.3 nm, consisting of tetragonal (P42/mnm) and orthorhombic (Pbcn) phases. We have evaluated different post‐synthesis purification methods to remove impurities such as chlorides and carbon‐hydrogen species. Each purification method exhibited unique advantages and side effects, providing insight into selecting the most appropriate method for specific applications. We also demonstrated the potential of these SnO2 nanoparticles as ethanol gas sensing materials and compared their performance with a commercial sensor.

GenRCA: a user-friendly rare codon analysis tool for comprehensive evaluation of codon usage preferences based on coding sequences in genomes

BackgroundThe study of codon usage bias is important for understanding gene expression, evolution and gene design, providing critical insights into the molecular processes that govern the function and regulation of genes. Codon Usage Bias (CUB) indices are valuable metrics for understanding codon usage patterns across different organisms without extensive experiments. Considering that there is no one-fits-all index for all species, a comprehensive platform supporting the calculation and analysis of multiple CUB indices for codon optimization is greatly needed.ResultsHere, we release GenRCA, an updated version of our previous Rare Codon Analysis Tool, as a free and user-friendly website for all-inclusive evaluation of codon usage preferences of coding sequences. In this study, we manually reviewed and implemented up to 31 codon preference indices, with 65 expression host organisms covered and batch processing of multiple gene sequences supported, aiming to improve the user experience and provide more comprehensive and efficient analysis.ConclusionsOur website fills a gap in the availability of comprehensive tools for species-specific CUB calculations, enabling researchers to thoroughly assess the protein expression level based on a comprehensive list of 31 indices and further guide the codon optimization.

Two-dimensional reinforcement learning model-free fault-tolerant control for batch processes against multi- faults

Aiming at the characteristics of batch process changing along with time and batch directions, the existence of unmodeled dynamics, and the partial failure of actuators or/and sensors, we propose a novel 2D reinforcement learning (RL) fault tolerant control strategy without considering model parameters. Firstly, a two-Dimensional (2D) augmented state space model and 2D Q function-based fault tolerant control (FTC) framework is established. The 2D Bellman equation can be acquired by analyzing the relationship between the 2D value function and the 2D Q function. Based on the extended model and Q-learning concept of RL, a data-driven FTTC independent of model parameters is designed, and a 2D data-driven Q-learning algorithm is proposed. Finally, taking the pressure holding phase in the injection process as the experimental object, the control effect is compared with that of the traditional model-based FTC, and better tracking performance and unbiasedness to the probing noise can be proved.

Lignocellulose Treatment Using a Flow-Through Variant of OrganoCat Process.

This study adapts the biphasic OrganoCat system into a flow-through (FT) reactor, using a heated tubular setup where a mixture of oxalic acid and 2-methyltetrahydrofuran (2-MTHF) is pumped through beech wood biomass. This method minimizes solvent-biomass contact time, facilitating rapid product removal and reducing the risk of secondary reactions. A comparative analysis with traditional batch processes reveals that the FT system, especially under severe conditions, significantly enhances extraction efficiency, yielding higher amounts of lignin and sugars with reduced solid residue. Notably, the FT system shows partial hydrolysis of the cellulose, which increases with temperature while not producing significant amounts of furfural or 5-HMF, indicating more efficient depolymerization of polysaccharides without substantial sugar degradation. A statistical design of experiments (DOE) using a Box-Behnken design elucidates the influence of process variables (time, solvent flow rate, temperature) on the yield. Key findings highlight reactor temperature as the dominant factor affecting yields, with process time showing a significant but less pronounced impact. This study demonstrates the potential of the FT OrganoCat system for efficient lignocellulosic biomass fractionation and represents an advancement towards continuous lignocellulose processing, contributing to our knowledge of process optimization for improved biorefinery applications.

Unraveling the impact of pH, sodium concentration, and medium osmolality on Vibrio natriegens in batch processes

BackgroundVibrio natriegens, a halophilic marine γ-proteobacterium, holds immense biotechnological potential due to its remarkably short generation time of under ten minutes. However, the highest growth rates have been primarily observed on complex media, which often suffer from batch-to-batch variability affecting process stability and performance. Consistent bioprocesses necessitate the use of chemically defined media, which are usually optimized for fermenters with pH and dissolved oxygen tension (DOT) regulation, both of which are not applied during early-stage cultivations in shake flasks or microtiter plates. Existing studies on V. natriegens’ growth on mineral media report partially conflicting results, and a comprehensive study examining the combined effects of pH buffering, sodium concentration, and medium osmolality is lacking.ResultsThis study evaluates the influence of sodium concentration, pH buffering, and medium osmolality on the growth of V. natriegens under unregulated small-scale conditions. The maximum growth rate, time of glucose depletion, as well as the onset of stationary phase were observed through online-monitoring the oxygen transfer rate. The results revealed optimal growth conditions at an initial pH of 8.0 with a minimum of 300 mM MOPS buffer for media containing 20 g/L glucose or 180 mM MOPS for media with 10 g/L glucose. Optimal sodium chloride supplementation was found to be between 7.5 and 15 g/L, lower than previously reported ranges. This is advantageous for reducing industrial corrosion issues. Additionally, an osmolality range of 1 to 1.6 Osmol/kg was determined to be optimal for growth. Under these optimized conditions, V. natriegens achieved a growth rate of 1.97 ± 0.13 1/h over a period of 1 h at 37 °C, the highest reported rate for this organism on a mineral medium.ConclusionThis study provides guidelines for cultivating V. natriegens in early-stage laboratory settings without pH and DOT regulation. The findings suggest a lower optimal sodium chloride range than previously reported and establish an osmolality window for optimal growth, thereby advancing the understanding of V. natriegens’ physiology. In addition, this study offers a foundation for future research into the effects of different ions and carbon sources on V. natriegens.

An Integrated Dynamic and Quality Modeling Framework for Batch Processes

This manuscript considers batch process operations and addresses the challenge of identifying a model that synergistically captures the dynamic input–output behavior of continuously measured variables along with the quality variables measured only at batch termination. To this end, an optimization-based framework is developed to identify one model that captures both the dynamics between the inputs and the continuously measured output variables, measurements of which are available at every time step, and the relation between the dynamic “state” information and the terminal quality measurements. Existing approaches either do not identify the dynamic and the quality model simultaneously, or they simply connect the whole trajectory of the process variables with the qualities and do not address the dynamic relationship between the inputs and the process variables. The improved modelling performance of the model obtained from this approach is demonstrated using data from a Uni-axial Rotational Molding process, and compared with existing modelling approaches.

Growth of Lactiplantibacillus plantarum BG112 in Batch and Continuous Culture with Camellia sinensis as Prebiotic

This work aimed to study the effect of Camellia sinensis extract (CSExt) as a particular growth promoter of Lactiplantibacillus plantarum (LP) in batch and continuous production processes. Growth conditions were 1% (v/v) inoculum, pHC = 6.5, 1% of dissolved oxygen (D.O.), 37 °C, and 150 rpm in a 0.2 L bioreactor using a commercial MRS broth (de Man, Rogosa, and Sharpe) and 1% (v/v) or 10% (v/v) CSExt according to the experimental design. In batch experiments, the maximum specific growth rate and the affinity constant increased with the increase in CSExt. In continuous culture, biomass production increased significantly with the addition of 1% (w/v) CSExt at 0.15 (1/h). Kinetic parameters adjusted were similar to those reported in the literature. Substrate affinity and the specific growth rate increased significantly in the presence of CSExt in batch and continuous cultures. Based on the results, prebiotics from plant extracts may function as growth promoters in specific physiological stages. This is the first report showing the change in kinetic parameters of a probiotic strain growing in crude plant extracts.

Multiphase nitrosation based on N2O3 intermediate and process intensification in a microflow reactor

Nitrosation of alcohols with nitrous acid is widely used to prepare alkyl nitrites, but the reaction mechanism and formation pathways of gaseous side products remain unclear. Here, we propose a nitrosation mechanism based on a highly reactive intermediate, dinitrogen trioxide (N2O3). The N2O3-based mechanism shows a lower energy barrier than the previously reported acid-catalyzed and nitrosyl cation mechanism. Furthermore, a microfluidic platform was established to investigate the gas–liquid-liquid reaction system and characterize the gas holdup of the multiphase reaction system. The results suggest that the reaction rate and selectivity primarily depend on the interfacial mass transfer of N2O3 between the aqueous-organic and gaseous-organic phases. A tubular microreactor was employed to intensify the nitrosation process of isopropanol. In contrast to the batch process operated by adding reactants dropwise in several hours, the product yield of 95 % can be achieved in a residence time of only 10 s in the microflow reactor.

Multiphase time-varying batch process monitoring based on stage moving model migration

To improve product qualities and obtain maximum comprehensive economic benefits from batch process production, it is of great practical significance to optimize the operation of batch production processes. Considering the multiphase and slow time-varying characteristics, a multiphase time-varying batch process monitoring method is proposed based on stage moving model migration technology. Firstly, the sliding windows are constructed with moving phases, where the moving of different phases is decided by the influence of the process variables to the final product. And time slices are the basic units for building the regression models. Secondly, to analyze and capture the relationship between the process variables of both the old mode and new mode and the final quality variable, the JY-PLS algorithm is utilized on each time slice within sliding windows for quality prediction. Then, monitoring indexes and corresponding control limits are calculated and used for process monitoring. The application to a real multiphase time-varying batch process, the start-up process of injection molding process, proved the effectiveness of the proposed method.

Prescribing a Green Future: A Review of Antimicrobial Waste in Pediatric Hospitals and Practices to Promote Healthcare Sustainability.

Pediatric hospitals are uniquely positioned to be impacted by antimicrobial waste. To explore this issue, we reviewed the current literature to identify the reasons, costs, and potential solutions to waste. Identified reasons for waste included weight-based dosing, medication order changes due to changing patient status, loss or expiration of doses, and medication errors. The cost of waste included financial costs, promotion of antimicrobial resistance, and generation of greenhouse gases. Proposed interventions to reduce waste included an early switch from intravenous to oral administration, required stop dates, standardized dosing times, and optimization of the pharmacy batching process. However, additional studies are needed to assess the potential correlation between these proposed interventions and waste reduction. Antimicrobial stewardship programs have been identified as a group that can play a crucial role in partnering to implement these interventions to potentially reduce antimicrobial waste and promote better healthcare sustainability.

Image based Modeling and Control for Batch Processes

This manuscript addresses the problem of leveraging thermal images for modeling and feedback control, specifically tailored for terminal quality control of batch processes. The primary objective, common in many batch processes, is to produce products with quality variables aligning with user specifications, available for measurement only at batch termination, precluding the direct use of classical control strategies. Furthermore, in many instances, traditional online sensors such as thermocouples may not be available, but instead spectral inputs like thermal images or acoustic data may be more readily available for feedback control. The challenge is to not only use the non-traditional sensor data for building a dynamic model but also to use that model for terminal quality control. The proposed approach involves a multi-layered modeling strategy. Initially, a dimensionality reduction technique is employed to condense the high-dimensional image into a set of representative outputs. Subsequently, subspace identification (SSID) is applied to develop a Linear Time-Invariant (LTI) State Space (SS) model between the inputs and the reduced outputs. Finally, a Partial Least Squares (PLS) model is constructed linking the terminal states of a batch (identified using SSID) with the product qualities obtained for that specific batch. This model is then incorporated into a Model Predictive Control (MPC) formulation. The effectiveness of the MPC is illustrated by showcasing its capability to generate products of high quality by deploying the MPC on a bi-axial lab-scale rotational molding setup.

Quality control of cardiac magnetic resonance imaging segmentation, feature tracking, aortic flow, and native T1 analysis using automated batch processing in the UK Biobank study.

Automated algorithms are regularly used to analyse cardiac magnetic resonance (CMR) images. Validating data output reliability from this method is crucial for enabling widespread adoption. We outline a visual quality control (VQC) process for image analysis using automated batch processing. We assess the performance of automated analysis and the reliability of replacing visual checks with statistical outlier (SO) removal approach in UK Biobank CMR scans. We included 1987 CMR scans from the UK Biobank COVID-19 imaging study. We used batch processing software (Circle Cardiovascular Imaging Inc.-CVI42) to automatically extract chamber volumetric data, strain, native T1, and aortic flow data. The automated analysis outputs (∼62 000 videos and 2000 images) were visually checked by six experienced clinicians using a standardized approach and a custom-built R Shiny app. Inter-observer variability was assessed. Data from scans passing VQC were compared with a SO removal QC method in a subset of healthy individuals (n = 1069). Automated segmentation was highly rated, with over 95% of scans passing VQC. Overall inter-observer agreement was very good (Gwet's AC2 0.91; 95% confidence interval 0.84, 0.94). No difference in overall data derived from VQC or SO removal in healthy individuals was observed. Automated image analysis using CVI42 prototypes for UK Biobank CMR scans demonstrated high quality. Larger UK Biobank data sets analysed using these automated algorithms do not require in-depth VQC. SO removal is sufficient as a QC measure, with operator discretion for visual checks based on population or research objectives.

Development of a Novel High Temperature Continuous Particle Mass Flow Measurement Device for Particle-Based CSP Applications

Particle-based concentrating solar power (CSP) technology is one of the generation three (Gen3) CSP technologies that has potential for lowering the levelized cost of electricity produced by this solar thermal power systems. Commercially available ceramic particles, which are rated for high temperature applications up to 1200 °C, were selected for this study. This medium is commonly used as an energy carrier or heat transfer medium in Gen3 CSP research and development efforts [1]. Prior studies at the National Solar Thermal Test Facility (NSTTF) have investigated and evaluated various mass flow measurement methods, and a simple gravimetric temporal load measurement method was chosen as the best candidate for the research purpose [2]. This mass flow measurement method is a batch process and is not a viable solution for commercial-scale power generation applications based on cost, space, process control, and practical system integration factors. In-line and continuous particle mass flow measurement will play an integral role in efficient and cost effective Gen3 CSP particle technologies. Process parameters, including energy absorbed by the particles, receiver efficiency, temperature dependent flow phenomena, and general high temperature measurement reliability are all rely on repeatable and accurate measurement of particle mass flow under various operating conditions. Ceramic particles, like those used in this application, are not conventionally used as an energy carrier. A novel concept for continuous particle mass flow at a wide range of operating temperatures is currently under development and planned to be tested at the NSTTF, called the Particle In-LinE (PILE) mass flow sensor.

Dairy liquid waste as substrate to obtain biotechnological chondroitin: A circular economy approach

Microbial polysaccharides have been gaining growing interest often as alternative to animal derived products or as sources of novel features for biotechnological applications. Process production costs, however, are still high. A possible solution to that exploits agri-food and dairy industrial byproducts as fermentation substrates. This approach also reduces the need for cost-intensive disposal treatments for these waste sources and supports green and circular economy policies. Therefore, as for other microbial glucuronic acid-based biopolymers (e.g. hyaluronic acid, alginate), in this perspective, wild type and engineered E. coli K4 were used in this work as cell factories to produce K4 capsular polysaccharide (CPS) from renewable sources. The backbone of the K4 CPS, chondroitin, is the precursor of chondroitin sulfate (CS), a glycosaminoglycan found in animal tissues that is extensively used for curing osteoarthritis and studied for several other emerging biomedical applications. Interestingly, also chondroitin showed promising bioactivity in vitro and in vivo. Due to its high availability from local companies, second cheese whey (SCW), a worldwide copious and polluting liquid waste, was used as fermentation substrate in this work. Results showed that SCW fully supports growth of wild type and recombinant E. coli K4 strains, and demonstrate, for the first time up to date, the production of K4 CPS from liquid waste as proof of principle. Batch processes in 3 L fermenters indicated a 100 % improvement of the polysaccharide yield and allowed the production of 1.1±0.1 g/L of product from the recombinant strain with very low accumulation of acetic acid, demonstrating that SCW by itself fully supports polysaccharide production.

Reactive Blue MEBF 222 dye and textile wastewater treatment using metal-doped cobalt and nickel perovskites by batch and column adsorption process.

Water contamination is a serious issue that has an impact on the whole globe. In the current work, adsorption technique was used to remove synthetic Reactive Blue MEBF 222 textile dye utilizing Cd-doped Co (Co1-xCd1.5xFeO3), Zn-doped Co (Co1-xZn1.5xFeO3), Cr-doped Co (Co1-xCr1.5xFeO3), Zn-doped Ni (Ni1-xZn1.5xFeO3), and Cr-doped Ni (Ni1-xCr1.5xFeO3) perovskites, synthesized by sol-gel auto-combustion approach. According to the findings of batch adsorption studies, maximum adsorption was observed at pH 3 (45.62mg/g), 0.01g/50ml dosage (36.67mg/g), 60min (14.31mg/g), 100ppm dye concentration (47.41mg/g), and 308K (35.96mg/g) for Co1-xCd1.5xFeO3; at 3 pH (42.94mg/g), 0.01g/50ml dosage (35.33mg/g), 60min (12.88mg/g), 100ppm dye concentration (40.52mg/g), and 308K (31.31mg/g) for Co1-xZn1.5xFeO3; at 2 pH (38.82mg/g), 0.01g/50ml dosage (32.20mg/g), 60min (11.98mg/g), 100ppm dye concentration (33.54mg/g), and 308K (29.34mg/g) for Co1-xCr1.5xFeO3; at 2 pH (34.97mg/g), 0.01g/50ml dosage (30.41mg/g), 60min (10.46mg/g), 100ppm dye concentration (27.19mg/g), and 308K (26.12mg/g) for Ni1-xZn1.5xFeO3; and at 2 pH (31.22mg/g), 0.01g/50ml dosage (25.04mg/g), 60min (9.48mg/g), 100ppm dye concentration (21.73mg/g), and 308K (23.61mg/g) for Ni1-xCr1.5xFeO3. The pseudo-second-order model showed good fitness for adsorption kinetic data. Electrolytes, detergents/surfactants, and heavy metal ions had a substantial impact on the adsorption potential. The column adsorption experiments demonstrated optimal bed height, flow rate, and intake dye concentration to be 3cm, 1.8ml/min, and 70mg/l, respectively, in the column experiment. With an adsorption capacity of 44.1mg/g, reactive blue (RB) 222 dye was able to achieve its maximum adsorption. Detailed desorption of RB 222 dye was also achieved. The novelty of this adsorption method lies in its eco-friendliness, ease of handling, and cost-effectiveness.

An adaptive shuffled frog-leaping algorithm for flexible flow shop scheduling problem with batch processing machines

Batch Processing Machines (BPM) and transportation are seldom studied simultaneously in Flexible Flow Shop. In this study, Flexible Flow Shop Scheduling Problem (FFSP) with BPM at the last stage and transportation is considered and an adaptive shuffled frog-leaping algorithm (ASFLA) is proposed to minimize makespan. To produce high-quality solutions, a heuristic is employed to produce initial solution, two groups are formed by using all memeplexes, then an adaptive memeplex search is implemented, in which the number of searches is dynamically determined by the quality of the memeplex, an adaptive group search is also conducted by exchanging memeplexes or supporting of the worse memeplex. A novel population shuffling and the worst memeplex elimination are proposed. A number of computational experiments are executed to test the new strategies and performances of ASFLA. Computational results demonstrate that new strategies are effective and ASFLA is a very competitive algorithm for FFSP with BPM and transportation.

Big Data Analytics using Artificial Intelligence: Apache Spark for Scalable Batch Processing

The rapid proliferation of data in the digital age has made big data analytics a critical tool for deriving insights and making informed decisions. However, processing and analyzing large datasets, often reaching hundreds of terabytes, presents significant challenges. This paper explores the use of Apache Spark, a powerful distributed computing framework, for batch processing in big data analytics using artificial intelligence (AI) techniques. We evaluate the scalability, efficiency, and accuracy of AI models when applied to massive datasets processed in Spark. Our experiments demonstrate that Apache Spark, coupled with machine learning and deep learning techniques, offers a robust solution for handling large-scale data analytics tasks. We also discuss the challenges associated with such large-scale processing and propose strategies for optimizing performance and resource utilization.

Scalable pilot production of highly efficient 5-ply respiratory masks enhanced by bacterial cellulose nanofibers

This study presents the pilot-scale production of highly efficient real respiratory masks enhanced by bacterial cellulose nanofibers (BCNFs). The BCNFs suspension was deposited onto tissue paper substrates using fog spray technique with three BCNFs grammage levels of 0.5, 1, and 2 g/m2, followed by freeze drying. Also, two continuous and batch welding processes have been used to construct the core structure of the masks. Field emission scanning electron microscopy (FE-SEM) confirmed the uniform distribution and size of fog-sprayed BCNFs and their pore networks. With increase in BCNFs grammage, the adsorption efficiency of masks increased in both continuous and batch production methods. The mask produced through batch processing showed the highest efficiency of 99.2 % (N99) for the particulate matter of 0.3 μm, while the maximum corresponding efficiency value in continuous processing was 95.4 % (N95). The pressure drops of the masks increased with the increase in BCNFs grammage in both methods. The maximum pressure drops of N95 and N99 masks obtained were 112 ± 10 Pa and 128 ± 8 Pa, respectively. Notably, the filtration efficacy of masks was preserved when subjected to relative humidity fluctuations ranging from 30 % to 70 %. The successful findings of this study offer significant promise for future air filtration applications.

One-step hot-press synthesis of amino functionalized Zr-based metal organic framework for determination and speciation of trace inorganic arsenic species in water samples with EDXRF spectrometry

Herein, amino functionalized Zr-based metal organic framework (UiO-66-NH2) on carbon cloth substrate was synthesized by using one-step hot-pressing method and the product (UiO-NH2@CC) was evaluated as a new solid phase extraction (SPE) material for determination and speciation of inorganic arsenic species (As(V) and As(III)) in water samples through energy dispersive X-ray fluorescence (EDXRF) spectrometry. SPE efficiency of UiO-NH2@CC was examined in batch and continuous adsorption (pre-concentration) processes and combination of either batch or continuous pre-concentration process with EDXRF spectrometry proposed analytical method, offering remarkable benefits (i.e., user-friendly, no elution procedure, environmentally friendly and cost-effective, etc.). Batch adsorption studies indicated that As(V) could be selectively adsorbed by UiO-NH2@CC at pH 2 and better represented by Freundlich isotherm model in 60 min of equilibrium time. Continuous adsorption studies at pH 2 demonstrated a drop in As(V) adsorption efficiency of UiO-NH2@CC for sample loading volume above 30 mL with increasing flow rate from 0.3 to 1.0 mL/min and initial As(V) concentration from 20 to 100 µg/L. By considering fortification experiments on real water samples at two levels (5 and 50 µg/L) of As(V) species, both batch and continuous pre-concentration steps coupled with EDXRF spectrometry, with LOD values of 0.790 and 0.220 µg/L, respectively, indicated good recoveries in the range of 87(±11)–104(±7) with no statistically significant differences. Finally, the continuous pre-concentration step followed by EDXRF measurement, as a representative analytical method, was of successful applicability to the speciation of As(III) and As(V) in real water samples with notable advantages (i.e., effortless, economical, practical and reproducible, etc.).

Sustainable synthesis of iron oxide nanopigments: Enhancing pigment quality through combined air oxidation and cavitation techniques

This study investigates the synthesis and characterization of iron oxide pigments through various methods, including air oxidation, co-precipitation, and hydrodynamic cavitation. The infrared (IR) spectral analysis revealed key functional groups and metal-oxide bonds indicative of goethite, with bands associated with O–H stretching and Fe–O vibrations. The study also explores how different conditions, such as temperature, pH, and reactant ratios, affect the color, yield, and particle size of the pigments produced. Through batch and continuous synthesis processes, it was found that cavitation offers a more controlled particle size distribution compared to other methods. The experiments highlight the influence of parameters like temperature, pH, and NaOH concentration on the final pigment properties, with optimized conditions yielding high-quality pigments. The comparison of synthetic methods demonstrates the trade-offs in terms of reaction complexity, particle size control, and yield, emphasizing the potential of hydrodynamic cavitation for efficient and scalable pigment production. The study concludes by summarizing the versatile reactions of FeSO4 and NaOH in producing various iron oxide and oxyhydroxide materials, which are valuable in industrial and environmental applications.