Batch Process Research Articles

SKYQUANT 3D: Quantifying Vascular Anatomy With an Open-Source Workflow for Comprehensive Analysis of Volumetric Optoacoustic Angiography Data.

Efficient visualization of the vascular system is of key importance in biomedical research into tumor angiogenesis, cerebrovascular alterations, and other angiopathies. Optoacoustic (OA) angiography offers a promising solution combining molecular optical contrast with high resolution and deep penetration of ultrasound. However, its hybrid nature implies complex data collection and processing workflows, with significant variability in methodologies across developers and users. To streamline interoperability, we introduce SKYQUANT 3D, a Python-based set of instructions for the Thermo Fisher Scientific Amira/Avizo 3D Visualization & Analysis Software. Our workflow simplifies the batch processing of volumetric optoacoustic angiography images, extracting meaningful quantitative information while also providing statistical analysis and graphical representation of the results. Quantification performance of SKYQUANT 3D is demonstrated using functional preclinical and clinical in vivo 3D OA angiographic tests involving ambient temperature variations and repositioning of the imaged limb.

Comparative Analysis of Thermal Activation on Felts and Continuous Carbon Filament Electrodes for Vanadium Redox Flow Batteries

AbstractThermal treatments are commonly used to improve electrode kinetics in vanadium redox flow batteries (VRFB). The impact of the widely adopted thermal treatment—400 °C for least 24 hours—was investigated on polyacrylonitrile (PAN)‐based continuous carbon filaments (tows) and compared to PAN‐based graphite felts. Surface properties were assessed with scanning electron microscopy (SEM), Raman spectroscopy, X‐ray photoelectron spectroscopy (XPS) and wettability measurements. The electrode activity was investigated via electrochemical impedance spectroscopy (EIS). Charge‐transfer resistances and the constant phase element parameters related to the electric double layer were determined, revealing a correlation between enhanced electrode activity and increased double layer across all electrodes. An 8‐hour 400 °C thermal treatment was sufficient to improve electrode activity for tows, whereas felts required longer durations, up to 24 hours, attributed to differences in the carbonization process employed for each material, with the tows undergoing continuous processing and the felts following a batch process. Three‐electrode half‐cell EIS measurements were conducted to elucidate positive and negative electrode contributions. Activated continuous carbon filament electrodes exhibited consistent electrode activities in both the catholyte (VO2+/VO2+) and anolyte (V3+/V2+), whereas the electrochemical activity of felts was limited by the electrode deactivation in the anolyte.

Visible-light-mediated flow protocol for Achmatowicz rearrangement.

The batch processes of APIs/pharmaceutical synthesis are prone to suffer significant limitations, including longer process time, shortage of skilled manpower, laborious post-synthetic work-up, etc. To address the inherent limitations of batch processes, a novel approach was undertaken, resulting in the establishment and development of a visible light-assisted modular photo-flow reactor with a seamlessly integrated post-synthetic work-up procedure enabling the efficient synthesis of dihydropyranones from furfuryl alcohols. The reaction uses sun light as green energy source, and the novel photo-flow reactor platform developed with an integrated system enabling a downstream process in a time and labor-efficient manner which facilitates the Achmatowicz rearrangement, resulting in a fast (10 min) formation of the dihydropyranone products.

The efficiency of CUSUM schemes for monitoring the multivariate coefficient of variation in short runs process

Current monitoring technologies emphasize and address the issue of monitoring high-volume production processes. The high flexibility and diversity of current industrial production processes make monitoring technology for small batch processes even more important. In multivariate process monitoring, a broader applicability exists in multivariate coefficients of variation (MCV) based monitoring schemes due to the lower restriction of the process. In view of the effectiveness of MCV monitoring and with the aim to achieve further performance improvement of current MCV monitoring schemes in a finite horizon production, we additionally introduce two one-sided cumulative sum (CUSUM) MCV schemes. In the case of deterministic and random shifts, the design parameters of the proposed schemes are obtained via an optimization procedure designed by the Markov chain method and the corresponding performance is analysed based on different run length (RL) characteristics, including the mean and the standard deviation. Simulation comparisons with existing exponentially weighted moving average (EWMA) MCV schemes show that the proposed CUSUM MCV schemes are more efficient in monitoring most of the shifts, including the deterministic and random shifts. Finally, to demonstrate the benefits of the new monitoring schemes, a comprehensive case study on monitoring a steel sleeve manufacturing process is conducted.

Photolytic ortho-Selective Amino Pyridylation of Aryl Isocyanates with N-Amino Pyridinium Ylides for the Synthesis of N-Arylsulfonyl Ureas.

Herein, we report an expedient synthesis of aryl sulfonyl ureas 4 and 5 from N-amino pyridinium ylides and aryl isocyanates. N-Aminopyridinium ylides 3 are synthesized via blue light-emitting diode irradiation of pyridine/isoquinoline and appropriate iminoiodinanes. The strategy involved a hitherto unknown carboamination of imine moieties (of aryl isocyanates) via a three-component reaction of pyridine derivatives/isoquinoline 1, N-aryl sulfonyl iminoiodinanes 2, and numerous aryl isocyanates at room temperature in 2-methyl tetrahydrofuran to afford the target compounds in moderate to excellent yields. N-Arylpyridinium ylides 3 (as intermediates) undergo a [3+2] cycloaddition with the aryl isocyanates followed by the aromatization of the pyridine/isoquinoline moiety to afford compounds 4. On the basis of the substitution pattern among the reactants, in some cases pyridine extrusion occurs during the reaction to afford depyridinylated aryl sulfonyl ureas 5. In general, isocyanates are used as dipolarophiles in [3+2] cycloaddition reactions. However, regioselective amino pyridylation of these species is a first. Control experiments and density functional theory calculations elucidate the reaction mechanism. The batch process of the protocol could be seamlessly transferred to the photoflow synthesis.

In silico PCR analysis: a comprehensive bioinformatics tool for enhancing nucleic acid amplification assays.

Nucleic acid amplification assays represent a pivotal category of methodologies for targeted sequence detection within contemporary biological research, boasting diverse utility in diagnostics, identification, and DNA sequencing. The foundational principles of these assays have been extrapolated to various simple and intricate nucleic acid amplification technologies. Concurrently, a burgeoning trend toward computational or virtual methodologies is exemplified by in silico PCR analysis. In silico PCR analysis is a valuable and productive adjunctive approach for ensuring primer or probe specificity across a broad spectrum of PCR applications encompassing gene discovery through homology analysis, molecular diagnostics, DNA profiling, and repeat sequence identification. The prediction of primer and probe sensitivity and specificity necessitates thorough database searches, accounting for an optimal balance of mismatch tolerance, sequence similarity, and thermal stability. This software facilitates in silico PCR analyses of both linear and circular DNA templates, including bisulfited treatment DNA, enabling multiple primer or probe searches within databases of varying scales alongside advanced search functionalities. This tool is suitable for processing batch files and is essential for automation when working with large amounts of data.

Digital twin-based multi-granularity synchronisation for production-warehousing under batch processing mode

Batch production is a cost-effective way that boosts efficiency in manufacturing systems. As customer demands become increasingly customised, there is a growing need to consolidate numerous small-batch orders. However, if batch production exceeds the optimal level, it may lead to the premature completion of customer orders, causing temporary congestion in the finished goods warehouse and prolonged resource occupation. Consequently, an essential focus of this study pertains to coordinating decision-making units like workshops and warehouses within batch production setups to ensure stable system operation amidst dynamic customer demands. The proposed solution encompasses a Digital Twin-based Multi-level Synchronised Control System (DTMCS) framework, featuring the Multi-granularity Synchronised Control Mechanism (MgSCM) and a collaborative optimisation model for batch production and warehouse planning. By examining a practical case study from a paint manufacturing company, the research delves into the effects of four synchronised control patterns on various system performance metrics, such as operating costs, equipment and warehouse utilisation rates, and system stability. Overall, this study provides an adaptable and cost-effective solution for enhancing production-logistics synchronisation.

Automated Optimization of a Multistep, Multiphase Continuous Flow Process for Pharmaceutical Synthesis.

Flow synthesis is becoming increasingly relevant as a sustainable and safe alternative to traditional batch processes, as reaction conditions that are not usually achievable in batch chemistry can be exploited (for example, higher temperatures and pressures). Telescoped continuous reactions have the potential to reduce waste by decreasing the number of separate unit operations (e.g., crystallization, filtration, washing, and drying), increase safety due to limiting operator interaction with potentially harmful materials that can be reacted in subsequent steps, minimize supply chain disruption, and reduce the need to store large inventories of intermediates as they can be synthesized on demand. Optimization of these flow processes leads to further efficiency when exploring new reactions, as with a higher yield comes higher purity, reduced waste, and a greener synthesis. This project explored a two-step process consisting of a three-phase heterogeneously catalyzed hydrogenation followed by a homogeneous amidation reaction. The steps were optimized individually and as a multistep telescoped process for yield using remote automated control via a Bayesian optimization algorithm and HPLC analysis to assess the performance of a reaction for a given set of experimental conditions. 2-MeTHF was selected as a green solvent throughout the process, and the heterogeneous step provided good atom economy due to the use of pure hydrogen gas as a reagent. This research highlights the benefits of using multistage automated optimization in the development of pharmaceutical syntheses. The combination of telescoping and optimization with automation allows for swift investigation of synthetic processes in a minimum number of experiments, leading to a reduction in the number of experiments performed and a large reduction in process mass intensity values.

Castanet: a pipeline for rapid analysis of targeted multi-pathogen genomic data.

Target enrichment strategies generate genomic data from multiple pathogens in a single process, greatly improving sensitivity over metagenomic sequencing and enabling cost-effective, high-throughput surveillance and clinical applications. However, uptake by research and clinical laboratories is constrained by an absence of computational tools that are specifically designed for the analysis of multi-pathogen enrichment sequence data. Here we present an analysis pipeline, Castanet, for use with multi-pathogen enrichment sequencing data. Castanet is designed to work with short-read data produced by existing targeted enrichment strategies, but can be readily deployed on any BAM file generated by another methodology. Also included are an optional graphical interface and installer script. In addition to genome reconstruction, Castanet reports method-specific metrics that enable quantification of capture efficiency, estimation of pathogen load, differentiation of low-level positives from contamination, and assessment of sequencing quality. Castanet can be used as a traditional end-to-end pipeline for consensus generation, but its strength lies in the ability to process a flexible, pre-defined set of pathogens of interest directly from multi-pathogen enrichment experiments. In our tests, Castanet consensus sequences were accurate reconstructions of reference sequences, including in instances where multiple strains of the same pathogen were present. Castanet performs effectively on standard computers and can process the entire output of a 96-sample enrichment sequencing run (50M reads) using a single batch process command, in $<$2 h. Source code freely available under GPL-3 license at https://github.com/MultipathogenGenomics/castanet, implemented in Python 3.10 and supported in Ubuntu Linux 22.04. The data underlying this article are available in Europe Nucleotide Archives, at https://www.ebi.ac.uk/ena/browser/view/PRJEB77004.

Bioremediation of textile wastewater using Bacillus cereus isolated from refinery site: Comparative analysis of free and immobilized cells systems

The textile industries consume a considerable amount of freshwater and discharge untreated or semi-treated pollutants to the environment. Azo dyes, generally employed in textile manufacturing industries, have a hazardous impact on the ecosystem. Incomplete treatment of wastewater containing azo dyes discharges into the aquatic system, which has recalcitrant and toxic properties. Hence, the need for cost-effective and high-performing technologies is essential. In this direction, bacterial degradation is considered an economically viable and efficient technique for the treatment of Active blue (AB) dye. Polyurethane foam (PUF) is a low-cost and hydrophilic material with a microporous structure, which offers an excellent surface-to-volume ratio. Hence, PUF was preferred for the immobilization of isolated bacterial species. For this, a dye-degrading gram-positive bacteria namely Bacillus cereus GS2 IIT (BHU) was isolated from the petroleum sludge. A comparative analysis between free and immobilized cell systems was carried out by varying the process parameters, i.e., batch time, pH, temperature, glucose concentration, and initial AB dye concentration. The corresponding optimum conditions were found to be 6.0 days, 6.5, 30 °C, 1.0 g/L, and 50 mg/L, respectively. The immobilized cell system exceeds the dye removal efficiency (RE) by 10. 7 % compared to the free cell system. The packed bed bioreactor could be able to deliver 98.56 % of dye removal at an inlet loading rate of 30 mg/L d and 50 mg/L of initial dye concentration.

Iterative learning robust security predictive tracking control for small delay batch processes: Deception attacks on unreliable network channel

During the batch process, the need for security control is becoming increasingly urgent with the gradual penetration of network control technology. For small time delay batch processes subject to deception attacks, an iterative learning robust security predictive tracking control approach is developed. Reviewing previous studies for iterative learning control, the issue that the repetitive character of the batch process would mask the characteristics of deception attacks was not considered. Moreover, the gains of iterative learning control law are utilized offline, which makes large deviations for the system state as the operating time increases. This will lead to “excessive-input” conditions for control inputs, which means more consumption of energy and production resources. To address these challenges, we introduce robust security invariant sets to improve the robustness of the system by constraining the system state to be in a safe range. Also, the design of the iterative learning controller incorporates deception attack data for historical batches, which is continuously improved and optimized to withstand deception attacks. Meanwhile, by calculating stability conditions online, the real-time control law gain is obtained, which could make the control inputs adjusted online based on the real-time system’s dynamic characteristics, avoiding excessive inputs and improving the efficiency of energy and resource utilization. Additionally, the stability analysis proves that the system is input to state stability. Ultimately, simulation verifies the effectiveness and feasibility of the developed method.

An adaptive imbalance robust graph embedding broad learning system fault diagnosis for imbalanced batch processes data

Modern batch processes develop toward higher levels, and devices usually work at normal conditions with generally very few faults. Therefore, fewer fault data are collected than normal data, which makes the normal and fault modes imbalanced, and the fault diagnosis model is incapable focusing on the minority of fault samples as much as on most normal samples, consequently leading to insufficient generalization ability for the model. For that, we propose an adaptive imbalance-robust graph embedding broad learning system (AI-RGEBLS) in this paper. Firstly, it achieves adaptive correction of unbalanced samples by niche technique and synthetic minority over-sampling technique (SMOTE) with improved Mahalanobis distance. Then, broad learning system (BLS) is regularized by graph embedding, and further introduced the L2,1 norm constraint, which makes it possible to enhance the robustness of the model while considering the local manifold information of the data in the feature extraction process. Finally, the incremental learning approach is applied to the model to avoid the disastrous forgetting problem caused by training the whole model from zero. The effectiveness of the proposed method is verified by penicillin fermentation process and semiconductor etching process. It can effectively improve the speed of model training and provide better fault diagnosis for imbalanced batch processes compared with the existing methods.

Workflow improvement and financial gain after integration of high-throughput sample processing system with flow cytometer in a high-volume pathology laboratory: Results from a prospective comparative study using Lean principles

BackgroundHighly efficient clinical laboratories are essential for monitoring many human illnesses. Ampath Laboratory Services, the largest pathology lab in South Africa, analyzes large numbers of peripheral blood samples for CD4 levels yearly. ObjectiveTo assess productivity and quality of a newer integrated automated solution, the BD FACSDuet™ Sample Preparation System/BD FACSLyric™ Flow Cytometer using conventional assessment methods and Lean concepts. Materials and methodsThis prospective study compared the performance of the BD FACS™ Sample Preparation Assistant [SPA] III and BD FACSCanto™ II Flow Cytometer with the newly introduced integrated system (BD FACSDuet™/BD FACSLyric™). They were validated for accuracy, precision, and external quality assessment. Process mapping and Lean assessment helped identify steps leading to waste. An economic model was developed to characterize workflow and economic impact associated with total daily hands-on time, processing time, and reworks. ResultsStrong linear correlation was present between both systems. Precision and accuracy studies revealed that all coefficient of variation (CV)% values were below 20 % of allowable limits. External proficiency assessments were within limits. The fully automated workflow of BD FACSDuet™/BD FACSLyric™ permitted better consistency with significantly shorter processing time and batch processing and reduced operator interventions. Lean assessment identified defects with motion, over-processing, waiting, and non-utilized talent. Significant reductions in hands-on and total daily processing time that could increase daily specimen testing efficiency and fewer reworks were associated with the BD FACSDuet™/BD FACSLyric™. Lean improvements translated to significant economic savings associated with operator costs and unnecessary reagent consumption. ConclusionBD FACSDuet™/BD FACSLyric™ is an accurate, reliable, and cost-effective fully automated system for high-volume flow cytometry labs that perform T-cell enumeration using a single-platform and single-tube approach.

Utilization of water chestnut waste for biohydrogen production and enhanced power generation by stacked microbial fuel cell

A novel two-stage approach combining dark fermentation with microbial fuel cell (MFC) technology is proposed which enable biohydrogen production and bioelectricity generation from residual substrate energy. In the present study, batch fermentation of water chestnut waste using Enterobacter aerogenes (MTCC 2822) resulted in the production of biohydrogen. In the batch process, the highest production was 3.2 L/L. Further, single-parameter optimization and multi-parameter optimization were conducted via Response Surface Methodology (RSM) using the Central Composite Design (CCD) model. The maximum biohydrogen reached 3.44 g/L with 55% COD removal. The biohydrogen yield was 7.163 g H2/kg CODreduced with a maximum production rate of 712 mL/L/h. Further, the waste fermentation medium or spent media was used as a substrate in a Microbial Fuel Cell (MFC) to produce power using Pseudomonas aeruginosa PA1_NCHU as inoculum. MFCs were operated with various concentrations of phosphate buffer in the anolyte. As the output is limited in a single MFC, MFCs were operated in parallel stacks to increase power output. A maximum of 28% increase in the power density was observed in stacked MFCs. The energy recovered from the dark fermentation process was around 10.39% and a single MFC was around 10.67%. Hence, this study highlights the innovative use of agricultural waste and the effective combination of dark fermentation with stacked MFC, presenting a sustainable method of maximizing biohydrogen production and bioelectricity from spent media. By leveraging stacked MFC configuration, the potential of higher power output is demonstrated, underscoring the significance of this integrated system for energy recovery.

Optimization of anaerobic filter reactor with zeolite immobilization media for dairy sludge waste treatment for biogas production

Abstract Sludge from the dairy industry’s wastewater treatment contains a high organic content and a strong odor. It is proposed to treat the sludge by an anaerobic digestion process using a continuous anaerobic filter reactor (AFR) equipped with media immobilization support and a combination of ORP and pH sensors. Three immobilized media were put into different AFRs. The process took place in a cylindrical reactor with a column height of 150 cm and diameter of 63 cm with a total volume of 30 L. The total period of the anaerobic digestion process took place 17 days after the batch process. When the experiment was carried out using a flow-rate of 5 L/day, the highest sCOD removal of 90.44 % was using bioring media. Meanwhile, using flow-rate of 8L/day the highest sCOD removal up to 88.05 % was achieved by using zeolite media. The highest average methane production was obtained 8 mL/day and 24 mL/day of zeolite media by using flow-rate 5 L/day and 8 L/day respectively and reduced HRT up to 90 hours. To ensure the cellular activity on degradation of organic compounds is measured by Oxidation Reduction Potential (ORP) value. In the first stage, the ORP value is -60.23 mV to -472.82 mV for a flow-rate of 5 L/day subsequently the second stage is -471.9 mV to -477.75 mV for a flow-rate of 8 L/day. Anaerobic filter reactors using zeolite media can retain the effect of influent perturbation with a high range of alteration of sCOD and high recovery.

Flow-Shop Scheduling Problem With Batch Processing Machines via Deep Reinforcement Learning for Industrial Internet of Things

Flow-Shop Scheduling Problem With Batch Processing Machines via Deep Reinforcement Learning for Industrial Internet of Things

Photosensitizer-Free Benzo[1,4]oxazin-2-one Synthesis by Continuous-Flow Photochemistry.

We report a photosynthetic method for producing 2H-benzo[b][1,4]oxazin-2-ones from aryl azides and α-ketoacids. This method is highly sustainable, requiring only visible light irradiation of the substrates and no external additives. Furthermore, we implemented a continuous-flow system to achieve efficient light irradiation and rapid mixing, significantly improving reaction efficiency and reducing reaction time compared to the batch process. The flow system enabled gram-scale synthesis. We also demonstrated the utility of the products, by employing the benzo[1,4]oxazin-2-one moiety as a directing group for C-H activation on the 3-aryl substituent. This green approach highlights the potential for developing environmentally friendly synthetic processes.

Hardware Acceleration for High-Volume Operations of CRYSTALS-Kyber and CRYSTALS-Dilithium

Many high-demand digital services need to perform several cryptographic operations, such as key exchange or security credentialing, in a concise amount of time. In turn, the security of some of these cryptographic schemes is threatened by advances in quantum computing, as quantum computer could break their security in the near future. Post-quantum cryptography (PQC) is an emerging field that studies cryptographic algorithms that resist such attacks. The National Institute of Standards and Technology (NIST) has selected the CRYSTALS-Kyber Key Encapsulation Mechanism and the CRYSTALS-Dilithium Digital Signature algorithm as primary PQC standards. In this article, we present field-programmable gate array (FPGA)-based hardware accelerators for high-volume operations of both schemes. We apply high-level synthesis (HLS) for hardware optimization, leveraging a batch processing approach to maximize the memory throughput and applying custom HLS logic to specific algorithmic components. Using reconfigurable FPGAs, we show that our hardware accelerators achieve speedups between 3 \(\times\) and 9 \(\times\) over software baseline implementations, even over ones leveraging CPU vector architectures. Furthermore, the methods used in this study can also be extended to the new CRYSTALS-based NIST FIPS drafts, ML-KEM and ML-DSA, with similar acceleration results.

A MATLAB platform for production planning in batch processing firms producing items that are jointly replenished

Rising levels of poverty have prompted manufacturers of non-durable consumer goods to explore opportunities at the lower end of the wealth spectrum. To enhance affordability for low-income consumers, these goods are often packaged in various sizes, including single-serve sachets. While numerous models and algorithms have been developed to determine the optimal frequency of manufacturing runs and packaging setups for the simultaneous replenishment of different pack sizes, there is a notable lack of literature addressing their implementation by small and medium-sized enterprises (SMEs) in this sector. This gap may be attributed to the absence of accessible source codes, a shortage of model experts, and the complexity involved in manual calculations. To address this issue, we developed a user-friendly MATLAB software platform based on Goyal’s algorithm, tailored specifically for SMEs. The software prioritizes simplicity, interactivity, and flexible visual displays. It was validated against examples from existing literature and through randomly generated scenarios. Users can easily compute the optimal number of manufacturing setups, packaging frequencies for each pack size, and the associated costs by inputting relevant data such as manufacturing setup, inventory holding and packaging setup costs, and demand for each pack size. Importantly, users do not need expertise in modeling to operate the software. However, it is designed for single-product deterministic environments; future research should explore multi-product and stochastic modeling scenarios.

A newly-constructed technology to remove and recover diethyl phthalate from wastewater by using the instant plasticization assembly ability of PVC

Diethyl phthalate (DEP) is a typical environmentally organic pollutant, widely used in the production process of polyvinyl chloride (PVC) to improve the flexibility of plastic materials. Its interaction with living organisms can inflict considerable harm to reproductive system functions. This research aims to utilize tetrahydrofuran (THF) to selectively break the chemical bonds in PVC molecules to provide more adsorption sites. Then incorporates the plasticizing assembly process of PVC to instantly remove and recover DEP from wastewater, achieving waste utilization, and sustainable environmental development. The research found that PTFR with a concentration of around 75 mg/L shows the best DEP removal efficiency. Sequencing batch processing removes more DEP compared to direct processing under the same material usage conditions. Furthermore, the recovery rate of DEP can reach over 90%. The technology demonstrates notable enhancements in removal efficiency and adsorption duration when compared to conventional adsorption techniques. This research has established an instant and efficient method for DEP removal, providing a new idea and technology for plasticizer treatment in practical wastewater.