Batch Process Research Articles

Active-Learning Reliability Analysis of Automotive Structures Based on Multi-Software Interaction in the MATLAB Environment

The reliability design of automotive structures is characterized by numerous variables and implicit responses. The traditional design of experiments for metamodel construction often requires manual adjustment of model parameters and extensive finite element analysis, resulting in inefficiency. To address these issues, active learning-based reliability methods are effective solutions. This study proposes an active-learning reliability analysis method based on multi-software interaction. Firstly, through secondary development of different software and MATLAB (version 2023a)’s batch processing function, a multi-software interactive reliability analysis method is developed, achieving automation in structural parametric design, finite element analysis and post-processing. This provides a more efficient and convenient platform for the implementation of active learning. Secondly, the polynomial chaos–kriging (PCK) active-learning method is introduced, combining the advantages of polynomial chaos expansion (PCE) and kriging. The PCK method captures the global behavior of the computational model using regression-based PCE and local variations using interpolation-based kriging. This metamodel is constructed with fewer training samples, effectively replacing the real multi-dimensional implicit response relations, thereby improving the efficiency of modeling and reliability analysis. Finally, the specific implementation scheme is detailed. The accuracy and efficiency of the proposed method are verified by a reliability engineering example of body-in-white bending and torsional stiffness.

Fast Algorithm of Passive Bistatic Radar Detection Based on Batches Processing of Sparse Representation and Recovery

In the passive bistatic radar (PBR) system,methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets.

Distributed Batch Learning of Growing Neural Gas for Quick and Efficient Clustering

Growing neural gas (GNG) has been widely used in topological mapping, clustering and unsupervised tasks. It starts from two random nodes and grows until it forms a topological network covering all data. The time required for growth depends on the total amount of data and the current network nodes. To accelerate growth, we introduce a novel distributed batch processing method to extract the rough distribution called Distributed Batch Learning Growing Neural Gas (DBL-GNG). First, instead of using a for loop in standard GNG, we adopt a batch learning approach to accelerate learning. To do this, we replace most of the standard equations with matrix calculations. Next, instead of starting with two random nodes, we start with multiple nodes in different distribution areas. Furthermore, we also propose to add multiple nodes to the network instead of adding them one by one. Finally, we introduce an edge cutting method to reduce unimportant links between nodes to obtain a better cluster network. We demonstrate DBL-GNG on multiple benchmark datasets. From the results, DBL-GNG performs faster than other GNG methods by at least 10 times. We also demonstrate the scalability of DBL-GNG by implementing a multi-scale batch learning process in it, named MS-DBL-GNG, which successfully obtains fast convergence results. In addition, we also demonstrate the dynamic data adaptation of DBL-GNG to 3D point cloud data. It is capable of processing and mapping topological nodes on point cloud objects in real time.

Robust Structure-Aware Graph-based Semi-Supervised Learning: Batch and Recursive Processing

Graph-based semi-supervised learning plays an important role in large scale image classification tasks. However, the problem becomes very challenging in the presence of noisy labels and outliers. Moreover, traditional robust semi-supervised learning solutions suffers from prohibitive computational burdens thus cannot be computed for streaming data. Motivated by that, we present a novel unified framework robust structure-aware semi-supervised learning called Unified RSSL (URSSL) for batch processing and recursive processing robust to both outliers and noisy labels. Particularly, URSSL applies joint semi-supervised dimensionality reduction with robust estimators and network sparse regularization simultaneously on the graph Laplacian matrix iteratively to preserve the intrinsic graph structure and ensure robustness to the compound noise. First, in order to relieve the influence from outliers, a novel semi-supervised robust dimensionality reduction is applied relying on robust estimators to suppress outliers. Meanwhile, to tackle noisy labels, the denoised graph similarity information is encoded into the network regularization. Moreover, by identifying strong relevance of dimensionality reduction and network regularization in the context of robust semi-supervised learning (RSSL), a two-step alternative optimization is derived to compute optimal solutions with guaranteed convergence. We further derive our framework to adapt to large scale semi-supervised learning particularly suitable for large scale image classification and demonstrate the model robustness under different adversarial attacks. For recursive processing, we rely on reparameterization to transform the formulation to unlock the challenging problem of robust streaming-based semi-supervised learning. Last but not least, we extend our solution into distributed solutions to resolve the challenging issue of distributed robust semi-supervised learning when images are captured by multiple cameras at different locations. Extensive experimental results demonstrate the promising performance of this framework when applied to multiple benchmark datasets with respect to state-of-the-art approaches for important applications in the areas of image classification and spam data analysis.

Process and production enhancement through codigestion in biogas generation

AbstractPakistan is facing a major challenge in the domestic gas and energy sector, and its demand is continuously growing. It is imperative to produce more energy in the form of gaseous resources and electricity to reduce this energy crisis in the future. The current work is related to studying various factors that play a critical role in enhancing the process and production of biogas. The effect of codigestion, substrate size, temperature, pH, and catalyst addition are important parameters. Three batch processes are conducted for 21 days under mesophilic conditions, which is easier to achieve as compared with the thermophilic one. Codigestion of cow manure combined with food, poultry waste, and sewerage water showed some promising results compared with a single substrate (cow dung). This results in the production of biogas of about 120 L. The particle size is then reduced to 5 mm, which leads to an increase in the available surface area for microbial attack and hence increases and enhances further the process and production of biogas. However, the addition of 250 g of silica gel increases production by up to 17%. The better value for the pH range for this batch was found in the range of 6.5–7.8. The codigestion would help in cost‐effective and more efficient waste treatment. The digestate in all the batch processes comes out enriched in nitrogen that is used as an organic fertilizer.

A novel lignin-steel sludge composite for dyes adsorption from water: Surface functionalities and structural alteration mechanisms

The study focuses on fabricating and utilizing a lignin-steel sludge magnetic carbon composite (SLNa) to efficiently remove two dyes from an aqueous medium. The SLNa composite was fabricated through a simple two-step strategy involving the activation and thermal treatment of lignin and steel sludge. The SLNa possessed lignin and steel sludge starring properties, viz. abundant surface functionalities, graphitic carbon matrix, and magnetic characteristics. The synergistic combination of lignin and steel sludge resulted in composite's high adsorption potential for Methylene Blue (MB; 153.92 mg/g) and Acid Orange 7 (AO7; 64.84 mg/g) in batch mode. Additionally, the rapid adsorption kinetics and effective dye removal for MB (> 99 %) and AO7 (72 %) from simulated effluent revealed the potential of fabricated adsorbent for water remediation. Furthermore, the composite's applicability for continuous adsorption was also evaluated through a series of dynamic flow experiments using fixed-bed column systems. The demonstrated efficacy of SLNa, viz., 48.18 and 15.00 mg/g for MB and AO7, respectively, at high adsorbent loading and lower dye concentrations, highlights its potential for scalable water treatment applications. Thus, the versatility and effectiveness of the novel composite underscore its potential for practical application in both batch and continuous water remediation processes.

Online scheduling problem of incompatible batch processing with deterioration effect and delivery time in the steel rolling process

As market competition intensifies, modern enterprises prioritize customer demands, leading to an increased emphasis on multi-variety and small-batch production. In this article, we study the online scheduling problem for incompatible batch processing with the deterioration effect to minimize the maximum delivery completion time in the billet rolling process, where the batch capacity and delivery capacity are unbounded. We assume that the processing time of job Jj is expressed as p j = a j ( A + Bt ) , where A > 0, B > 0, t denotes the start time of job and aj > 0 represents the job’s processing deterioration rate. For such an online problem, we first prove that the lower bound of the problem is ( 1 + B a max ) k , where k denotes the number of job families and a max represents the maximum deterioration ratio in job instance. Then we design an online algorithm Largest Delivery Time With Batch Processing and demonstrate that the competitive ratio of the algorithm is no more than 1 + ( 1 + B a max ) k . Finally, experiments on randomly generated instances prove that our algorithm is reasonable and effective.

Product Design for Batch Processes Based on Iterative Learning-Latent Variable Model Inversion (IL-LVMI)

Product Design for Batch Processes Based on Iterative Learning-Latent Variable Model Inversion (IL-LVMI)

Heterogeneous Calcium Oxide Catalytic Filaments for Three-Dimensional Printing: Preparation, Characterization, and Use in Methyl Ester Production.

This study aimed to investigate heterogeneous catalytic filaments of calcium oxide (CaO) for fused deposition modeling three-dimensional (3D) printers. The CaO catalysts were blended with acrylonitrile butadiene styrene (ABS) plastic to form catalytic filaments. A single-screw filament extruder was used to prepare the filaments, following which their mechanical properties, thermal properties, morphology, catalytic characteristics in biodiesel production, and reusability were evaluated. In accordance with the results, a maximum CaO catalyst content of 15 wt % was recommended to be blended in the ABS pellet. The hardness and compressive strength of these catalytic filaments were shown to be improved. Subsequently, the catalytic filaments with the highest CaO content (15 wt %) were used to produce methyl ester from pretreated sludge palm oil through the transesterification process. To determine the recommended conditions for achieving the highest purity of methyl ester in biodiesel, the process parameters were optimized. A methyl ester purity of 96.58 wt % and a biodiesel yield of 79.7 wt % could be achieved under the recommended conditions of a 9.0:1 methanol to oil molar ratio, 75.0 wt % catalytic filament loading, and 4.0 h reaction time. Furthermore, the reusability of the 15 wt % CaO catalytic filaments was evaluated in a batch process with multiple transesterification cycles. The results indicated that the purity of methyl ester dropped to 95.0 wt % only after the fourth cycle. The method used in this study for preparing and characterizing CaO catalytic filaments can potentially serve as a novel approach for constructing biodiesel reactors using 3D printing technology.

Citrate synthase variants improve yield of acetyl-CoA derived 3-hydroxybutyrate in Escherichia coli

BackgroundThe microbial chiral product (R)-3-hydroxybutyrate (3-HB) is a gateway to several industrial and medical compounds. Acetyl-CoA is the key precursor for 3-HB, and several native pathways compete with 3-HB production. The principal competing pathway in wild-type Escherichia coli for acetyl-CoA is mediated by citrate synthase (coded by gltA), which directs over 60% of the acetyl-CoA into the tricarboxylic acid cycle. Eliminating citrate synthase activity (deletion of gltA) prevents growth on glucose as the sole carbon source. In this study, an alternative approach is used to generate an increased yield of 3-HB: citrate synthase activity is reduced but not eliminated by targeted substitutions in the chromosomally expressed enzyme.ResultsFive E. coli GltA variants were examined for 3-HB production via heterologous overexpression of a thiolase (phaA) and NADPH-dependent acetoacetyl-CoA reductase (phaB) from Cupriavidus necator. In shake flask studies, four variants showed nearly 5-fold greater 3-HB yield compared to the wild-type, although pyruvate accumulated. Overexpression of either native thioesterases TesB or YciA eliminated pyruvate formation, but diverted acetyl-CoA towards acetate formation. Overexpression of pantothenate kinase similarly decreased pyruvate formation but did not improve 3-HB yield. Controlled batch studies at the 1.25 L scale demonstrated that the GltA[A267T] variant produced the greatest 3-HB titer of 4.9 g/L with a yield of 0.17 g/g. In a phosphate-starved repeated batch process, E. coli ldhA poxB pta-ackA gltA::gltA[A267T] generated 15.9 g/L 3-HB (effective concentration of 21.3 g/L with dilution) with yield of 0.16 g/g from glucose as the sole carbon source.ConclusionsThis study demonstrates that GltA variants offer a means to affect the generation of acetyl-CoA derived products. This approach should benefit a wide range of acetyl-CoA derived biochemical products in E. coli and other microbes. Enhancing substrate affinity of the introduced pathway genes like thiolase towards acetyl-CoA will likely further increase the flux towards 3-HB while reducing pyruvate and acetate accumulation.

A 3D Parameterized BIM-Modeling Method for Complex Engineering Structures in Building Construction Projects

The structural components of large-scale public construction projects are more complex than those of ordinary residential buildings, with irregular and diverse components, as well as a large number of repetitive structural elements, which increase the difficulty of BIM-modeling operations. Additionally, there is a significant amount of inherent parameter information in the construction process, which puts forward higher requirements for the application and management capabilities of BIM technology. However, the current BIM software still has deficiencies in the parameterization of complex and irregular structural components, fine modeling, and project management information. To address these issues, this paper takes Grasshopper as the core parametric tool and Revit as the carrier of component attribute information. It investigates the parametric modeling logic of Grasshopper and combines the concepts of parameterization, modularization, standardization, and engineering practicality to create a series of parametric programs for complex structural components in building projects. This approach mainly addresses intricate challenges pertaining to the parametric structural shapes (including batch processing) and parametric structural attributes (including the batch processing of diverse attribute parameters), thereby ensuring the efficiency in BIM modeling throughout the design and construction phases of complex building projects.

Revolutionizing Dye Removal: Unleashing the Power of Liquid–Liquid Extraction Batch Process

Revolutionizing Dye Removal: Unleashing the Power of Liquid–Liquid Extraction Batch Process

Photocatalytic activity of ZnO doped Nano hydroxyapatite/GO derived from waste oyster shells for removal of Methylene blue.

Hydroxyapatite (HAp) stands as an inorganic compound, recognized as a non-toxic, bioactive ceramic, and its composition closely resembles that of bone material. In this study, nHAp was prepared from waste oyster shells, which are biowaste rich in calcium carbonate. nHAp with its unique catalytic property can be used as an adsorbent in various fields, including wastewater treatment. nHAp with an exceptional surface adsorbent with excellent chemical stability, enabling its catalytic function. Nano hydroxyapatite doped with Zinc oxide (ZnO) by wet chemical precipitation and made into a composite with Graphene oxide (GO) by modified hummers method followed by grinding, which was taken as 9:1 ratio (nHAp/ZnO and GO) of weight, enhances its tensile and mechanical strength. The energy band gap of nHAp photocatalyst was evaluated as 3.39eV and that of the in nHAp/ZnO/GO photocatalyst was narrowed to 1.77eV. The ternary nanocomposites are very efficient in generating the photogenerated electrons and holes, thereby improving the degradation potential of dye effluents to by-products such as CO2 and H2O. The nanocomposites photocatalyst were characterized by FTIR, XRD, SEM, TEM, EDS, XPS, DRS, and BET techniques. The UV-visible study shows the complete dye degradation efficiency of the prepared nanocomposites photocatalyst. In this study, the prepared nanocomposites nHAp/ZnO/GO have studied their efficiency for the removal of MB dye in a batch process by varying the dosage from 0.1 to 0.5g, and the effects of dosage variations, pH, kinetic, scavenger study were evaluated at a time interval of 30min. The removal of dye was found to be 99% at 150min of 0.3g dosage and pH = 12 is most favorable as it reached the same percentage at 90min. The as-prepared nanocomposite nHAp/ZnO/GO fits the kinetic rate constant equation and shows a pseudo-first-order reaction model. This study indicates the suitability for dye removal due to the synergistic effect and electrostatic interaction of the synthesized ternary nanocomposite, which shows the potential, socially active, low-cost-effective, eco-friendly, and safe for photocatalytic degradation of MB from wastewater.

Interactive Deep Image Colorization of Quality

Deep Image Colonization is a pioneering project aimed to revolutionizing the field of automated image colorization, particularly focusing on enhancing grayscale photographs' visual appeal and historical significance. Leveraging advanced deep learning models like VGG16 and UNET GAN, the project seeks to accurately and faithfully Add images in black and white some color. Through meticulous evaluation and comparison of different colorization algorithms, including real-time display of results and batch processing capabilities, the project strives to provide users with a seamless and intuitive experience. Beyond aesthetic enhancement, the project explores the implications of automated image colorization in various domains, from historical image restoration to creative visual storytelling. By evaluating colorization accuracy and refining models for real-world usage, the project aims to contribute to the advancement of image processing technologies. Ultimately, "Interactive Deep Image Colonization of Quality" endeavour to fill the void left by the past and the present, providing monochromatic imagery through vibrant hues and precision colorization techniques.

Integrating Image Analysis and Machine Learning for Moisture Prediction and Appearance Quality Evaluation: A Case Study of Kiwifruit Drying Pretreatment.

The appearance of dried fruit clearly influences the consumer's perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance.

IntegratingOLAP with NoSQL Databases in Big Data Environments: Systematic Mapping

The growing importance of data analytics is leading to a shift in data management strategy at many companies, moving away from simple data storage towards adopting Online Analytical Processing (OLAP) query analysis. Concurrently, NoSQL databases are gaining ground as the preferred choice for storing and querying analytical data. This article presents a comprehensive, systematic mapping, aiming to consolidate research efforts related to the integration of OLAP with NoSQL databases in Big Data environments. After identifying 1646 initial research studies from scientific digital repositories, a thorough examination of their content resulted in the acceptance of 22 studies. Utilizing the snowballing technique, an additional three studies were selected, culminating in a final corpus of twenty-five relevant articles. This review addresses the growing importance of leveraging NoSQL databases for OLAP query analysis in response to increasing data analytics demands. By identifying the most commonly used NoSQL databases with OLAP, such as column-oriented and document-oriented, prevalent OLAP modeling methods, such as Relational Online Analytical Processing (ROLAP) and Multidimensional Online Analytical Processing (MOLAP), and suggested models for batch and real-time processing, among other results, this research provides a roadmap for organizations navigating the integration of OLAP with NoSQL. Additionally, exploring computational resource requirements and performance benchmarks facilitates informed decision making and promotes advancements in Big Data analytics. The main findings of this review provide valuable insights and updated information regarding the integration of OLAP cubes with NoSQL databases to benefit future research, industry practitioners, and academia alike. This consolidation of research efforts not only promotes innovative solutions but also promises reduced operational costs compared to traditional database systems.

Adaptive soft sensor using stacking approximate kernel based BLS for batch processes

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms.

Rapid quantitative determination method of four heavy metals in strawberry

A rapid quantitative determination method of mercury, arsenic and arsenic, cadmium and chromium in strawberry was established. The sample to be tested was prepared into the solution to be tested, and the content of mercury, arsenic, cadmium and chromium were determined respectively on the machine. The results showed that the correlation coefficient of the standard curves of the four heavy metals was within 0.9961.000, and the detection limit was 0 mercury 0.028 g / kg, arsenic 0.062 g / kg, cadmium 0.14 g / kg, chromium 4.76 g / kg; the recovery was 80.2% 108.3% and 1.58% 26.34%; the test results of the standard samples were within the standard value. The method established in this experiment can determine the four heavy metals of mercury, arsenic, cadmium and chromium, etc. It has good linear correlation, low detection limit, good accuracy, high safety, simple operation, batch processing, and saves reagent consumables and detection time.

Porous interlocking assembly: performance-based dry masonry construction with digital stereotomy

Architected porosity in masonry structures can be created by transforming stock materials into a lattice of interlocking units through an automated batch process. Porous masonry forms numerous enclosed cavities for thermal performance and reduces material usage while maintaining structural integrity. This work investigates the potential and limits of digital tectonics of porous masonry through a complete process of design, manufacturing, and construction. The confluence of digital fabrication with tectonic exploration opens new dimensions unattainable by traditional stereotomy. Interlocking materials inspired by Abeille vault and digital stereotomy have made rapid progress. Following the theory of poetic construction, this work proposes that masonry construction should evoke visual or haptic enhancement through the fulfillment of pragmatic functions. We formulated a design challenge for a confined dry masonry wall for the envelope of the 2226 building. It assumes batch-cutting bespoke units out of large blocks of high-strength foam. Through a process of cutting and reassembling, the stock material is topologically expanded into a porous structure. A series of prototypes were developed to explore novel articulation, structural and thermal performance, and economical manufacturing. One can perceive the logic of porous construction through visual and haptic empathy. The materialization process interacts with the design masonry units and the interlocking mechanism. For future practice in masonry, the porosity should be planned at multiple scales (molecular scale, aggerate scale, construction scale) across the life cycle of the material.

Process optimization and economic evaluation of continuous multi-column capture for monoclonal antibody – A case study

In recent years, due to significant improvements achieved in upstream processing of monoclonal antibody (mAb) manufacturing, the focus on productivity and cost-effectiveness has shifted towards downstream processing. Protein A affinity chromatography has been widely employed as the capture step for mAb purification and serves as a key driver of economic efficiency on downstream operations. To enhance the purification process and alleviate manufacturing constraints while reducing costs, continuous processing, such as multi-column capture (MCC), has gained attention. This study aimed to establish and optimize the protein A MCC process and to evaluate it with traditional single-column capture mode. Through a series of verification runs, it was determined that there was no significant difference in yield and quality attributes across different loops. Furthermore, annual cost of goods (COGs) was evaluated with the conventional single-column batch process. The results showed that MCC process reduced operation cost by 18 % and almost doubled productivity in the specific scenario.