Batch Process Analysis Research Articles

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Fine-Scale Modeling and Monitoring of Wide-Range Nonstationary Batch Processes With Dynamic Analytics

Batch manufacturing has attracted extensive attention as a crucial way in process industry. It has typical wide-range nonstationary characteristics subject to time variant conditions, not only within each batch but also for different batches. The presence of closed-loop control complicates the task of batch process monitoring which shows that closed-loop control can significantly alter the process characteristics with both static deviations and varying temporal correlations. It is of a considerable challenge for batch process analysis and monitoring under closed-loop control which may work quite unlike that under open-loop control. In this article, a fine-scale process modeling and monitoring method are developed with dynamics analytics for wide-range nonstationary batch manufacturing with duration uncertainty. Both static and dynamic monitoring statistics are designed with clear physical interpretation which can work together to achieve fine-scale batch process status identification and monitoring. On one hand, it can identify frequent normal switching of operation status at different time by simultaneously checking both static and dynamic deviations regulated by closed-loop control. On the other hand, it can detect small disturbances in early stage where the impact may be overshadowed by the regulation of closed-loop controllers, making faults difficult to detect in time by conventional methods. Experiments have shown the validity of the proposed fine-scale monitoring method for wide-range batch process.

Big Data Processing-Beyond Batch Processing

This paper mainly focus on analysis of large sets of students data with one of the batch processing analysis techniques Beyond batch process, analysis of data streaming is done based on program of word counting program which executes data with HDFS along with dynamic created data. To compute similar coherent strategies one can implement a schema named batch and streaming process which dynamically creates data. The architecture is reduced to serve as X-Platform which uses ample number of tools for batch and stream analysis on this proposed frame work. Here we use spark-sql, a query language which acts as interface for interactive process to have iterative processes. Real time streaming data processing involves spark streaming works. Here we focus on preliminary evaluation of results and analysis report which compares data sets performance and also achieve low latency rate with usage of RDD.

Automated Live-Cell Imaging of Synapses in Rat and Human Neuronal Cultures.

Synapse loss and dendritic damage correlate with cognitive decline in many neurodegenerative diseases, underlie neurodevelopmental disorders, and are associated with environmental and drug-induced CNS toxicities. However, screening assays designed to measure loss of synaptic connections between live cells are lacking. Here, we describe the design and validation of automated synaptic imaging assay (ASIA), an efficient approach to label, image, and analyze synapses between live neurons. Using viral transduction to express fluorescent proteins that label synapses and an automated computer-controlled microscope, we developed a method to identify agents that regulate synapse number. ASIA is compatible with both confocal and wide-field microscopy; wide-field image acquisition is faster but requires a deconvolution step in the analysis. Both types of images feed into batch processing analysis software that can be run on ImageJ, CellProfiler, and MetaMorph platforms. Primary analysis endpoints are the number of structural synapses and cell viability. Thus, overt cell death is differentiated from subtle changes in synapse density, an important distinction when studying neurodegenerative processes. In rat hippocampal cultures treated for 24 h with 100 μM 2-bromopalmitic acid (2-BP), a compound that prevents clustering of postsynaptic density 95 (PSD95), ASIA reliably detected loss of postsynaptic density 95-enhanced green fluorescent protein (PSD95-eGFP)-labeled synapses in the absence of cell death. In contrast, treatment with 100 μM glutamate produced synapse loss and significant cell death, determined from morphological changes in a binary image created from co-expressed mCherry. Treatment with 3 mM lithium for 24 h significantly increased the number of fluorescent puncta, showing that ASIA also detects synaptogenesis. Proof of concept studies show that cell-specific promoters enable the selective study of inhibitory or principal neurons and that alternative reporter constructs enable quantification of GABAergic or glutamatergic synapses. ASIA can also be used to study synapse loss between human induced pluripotent stem cell (iPSC)-derived cortical neurons. Significant synapse loss in the absence of cell death was detected in the iPSC-derived neuronal cultures treated with either 100 μM 2-BP or 100 μM glutamate for 24 h, while 300 μM glutamate produced synapse loss and cell death. ASIA shows promise for identifying agents that evoke synaptic toxicities and screening for compounds that prevent or reverse synapse loss.

A high-throughput imaging and nuclear segmentation analysis protocol for cleared 3D culture models

Imaging and subsequent segmentation analysis in three-dimensional (3D) culture models are complicated by the light scattering that occurs when collecting fluorescent signal through multiple cell and extracellular matrix layers. For 3D cell culture models to be usable for drug discovery, effective and efficient imaging and analysis protocols need to be developed that enable high-throughput data acquisition and quantitative analysis of fluorescent signal. Here we report the first high-throughput protocol for optical clearing of spheroids, fluorescent high-content confocal imaging, 3D nuclear segmentation, and post-segmentation analysis. We demonstrate nuclear segmentation in multiple cell types, with accurate identification of fluorescently-labeled subpopulations, and develop a metric to assess the ability of clearing to improve nuclear segmentation deep within the tissue. Ultimately this analysis pipeline allows for previously unattainable segmentation throughput of 3D culture models due to increased sample clarity and optimized batch-processing analysis.

A Unifying and Integrated Framework for Feature Oriented Analysis of Batch Processes

We present a data analytics framework for offline analysis of batch processes. The framework provides a unified setting for implementing several variants of feature oriented analysis proposed in the literature, including a new methodology based on the process variables’ profiles presented in this article. It also integrates feature generation and feature analysis, in order to speed up the data exploration cycle, which is especially relevant for complex batch processes. The FOBA (Feature Oriented Batch Analytics platform) is described in detail and applied to several case studies regarding different analysis goals: visualization of the differences between the operation of two industrial units (dryers), quality prediction, and end-of-batch process monitoring. The performance of the proposed methodology is also critically assessed and compared with other alternative analytical approaches currently in use.

Simultaneously enhanced production and molecular weight of pullulan using a two‐stage agitation speed control strategy

AbstractBACKGROUNDPullulan with high molecular weight (Mw) is attractive for industrial application. Previous studies indicated that high yield and high Mw of pullulan cannot be simultaneously obtained under constant agitation speeds or constant dissolved oxygen levels during batch pullulan fermentation. In this study, a two‐stage agitation speed control strategy was developed to obtain pullulan with both high yield and high Mw.RESULTSBatch pullulan production at various constant agitation speeds ranging from 200 to 600 rpm was carried out in a 5 L stirred tank fermentor. Based on kinetic analysis of batch processes, activity assays of the enzymes associated with pullulan biosynthesis and biodegradation, and intracellular energy supply under different constant agitation speeds, a two‐stage control strategy that shifted the agitation speed from 500 to 400 rpm at 18 h proved to effectively achieve simultaneous high yield of high Mw pullulan.CONCLUSIONA maximum pullulan concentration of 27.4 g L−1 and final Mw of 3.09 × 106 Da were obtained using the two‐stage agitation speed control strategy, which were both greatly improved compared with those obtained at constant agitation speeds. © 2014 Society of Chemical Industry

Multiway elastic net (MEN) for final product quality prediction and quality-related analysis of batch processes

In batch processes, the final product quality is determined by the trajectories of the process variables throughout each batch. Consequently, there are two important issues that should be considered in the quality-related modeling. First, the process variable trajectories usually contribute to the final product quality cumulatively along the operation time within each batch. Such effect is named as the cumulative effect. Second, each process variable may have different impacts on the product quality at different time intervals, which is denoted as the time-varying effect. In order to model both two effects reasonably, a multiway elastic net (MEN) method is proposed in this paper. Accordingly, a quality prediction and process analysis scheme is presented. MEN integrates variable selection and regression in batch process modeling, where the regression coefficients are regularized in an automatic manner. With proper data pre-treatment, MEN can provide both accurate prediction and good interpretation. For online prediction, a future data estimation approach is proposed based on the k-nearest neighbor technique. The application of the proposed scheme to an injection molding process shows that MEN is not only effective in the online quality prediction but also enhance the understanding of the process.

Phase Analysis and Identification Method for Multiphase Batch Processes with Partitioning Multi-way Principal Component Analysis (MPCA) Model

Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.

CORSEN, a new software dedicated to microscope-based 3D distance measurements: mRNA–mitochondria distance, from single-cell to population analyses

Recent improvements in microscopy technology allow detection of single molecules of RNA, but tools for large-scale automatic analyses of particle distributions are lacking. An increasing number of imaging studies emphasize the importance of mRNA localization in the definition of cell territory or the biogenesis of cell compartments. CORSEN is a new tool dedicated to three-dimensional (3D) distance measurements from imaging experiments especially developed to access the minimal distance between RNA molecules and cellular compartment markers. CORSEN includes a 3D segmentation algorithm allowing the extraction and the characterization of the cellular objects to be processed--surface determination, aggregate decomposition--for minimal distance calculations. CORSEN's main contribution lies in exploratory statistical analysis, cell population characterization, and high-throughput assays that are made possible by the implementation of a batch process analysis. We highlighted CORSEN's utility for the study of relative positions of mRNA molecules and mitochondria: CORSEN clearly discriminates mRNA localized to the vicinity of mitochondria from those that are translated on free cytoplasmic polysomes. Moreover, it quantifies the cell-to-cell variations of mRNA localization and emphasizes the necessity for statistical approaches. This method can be extended to assess the evolution of the distance between specific mRNAs and other cellular structures in different cellular contexts. CORSEN was designed for the biologist community with the concern to provide an easy-to-use and highly flexible tool that can be applied for diverse distance quantification issues.

An Analytical Multimodulus Algorithm for Blind Demodulation in a Time-Varying MIMO Channel Context

This paper addresses the issue of blind multiple-input multiple-output (MIMO) demodulation of communication signals, with time-varying channels and in an interception context. A new adaptive-blind source separation algorithm, which is based on the implementation of the Multimodulus cost function by analytical methods, is proposed. First a batch processing analysis is performed; then an adaptive implementation of the (Analytical Multi-Modulus Algorithm) AMMA and its simplified version named (Analytical Simplified Constant Modulus Algorithm) ASCMA is detailed. These algorithms, named adaptive-AMMA and adaptive-ASCMA, respectively, are compared with the adaptive (Analytical Constant Modulus Algorithm) ACMA and the MMA (Multi-Modulus Algorithm). The adaptive-AMMA and adaptive-ASCMA achieve a lower residual intersymbol interference and bit error rate than those of the adaptive-ACMA and MMA.

Consideration of some sampling problems in the on-line analysis of batch processes by low-field NMR spectrometry

A low-field medium-resolution NMR spectrometer, with an operating frequency of 29 MHz for 1H, has been assessed for on-line process analysis. A flow cell that incorporates a pre-magnetisation region has been developed to minimise the decrease in the signal owing to incomplete polarisation effects. The homogeneous esterification reaction of crotonic acid and 2-butanol was monitored using a simple sampling loop; it was possible to monitor the progression of the reaction through changes in CH signal areas of butanol and butyl crotonate. On-line analysis of heterogeneous water-toluene mixtures proved more challenging and a fast sampling loop system was devised for use with a 5 L reactor. The fast sampling loop operated at a flow rate of 8 L min(-1) and a secondary sampling loop was used to pass a sub-sample through the NMR analyser at a slower (mL min(-1)) rate. It was shown that even with super-isokinetic sampling conditions, unrepresentative sampling could occur owing to inadequate mixing in the reactor. However, it was still possible to relate the 1H NMR signal obtained at a flow rate of 60 mL min(-1) to the composition of the reactor contents.

Self-Organizing Maps for Analysis of Expandable Polystyrene Batch Process

Self-organizing maps (SOM) have been successfully applied in many fields of research. In this paper, we demonstrate the use of SOM-based method for the analysis of Expandable PolyStyrene (EPS) batch process. To this end, a data set of EPS-batch process was used for training a SOM. Reference vectors of the SOM were then classified by K-means algorithm into six clusters, which represent product types of the process. This SOM could also be used for estimating the optimal amounts of the stabilisation agent. The results of a validation data set showed a good agreement between the actual and estimated amounts of the stabilisation agent. Based on this model a Web application was made for test use at the plant. The results indicate that the SOM method can also be efficiently applied to the analysis of the batch process.

One independent variable rate equation describing utilization of biodegradable organic matter in activated sludge processes

This investigation aims to represent aerobic utilization of biodegradable organic matter present in wastewater by a rate equation. This rate equation can then be used to develop a substrate utilization (removal) kinetic model for unsteady state activated sludge process. To achieve this objective, theoretical utilization of biodegradable organic matter in batch process and growth pattern theory were studied. Also, experimental data representing removal of organic matter in different types of wastewaters were collected and analyzed for batch and continuous activated sludge assays. A rate equation was proposed to describe the utilization of biodegradable organic matter based on theoretical analysis of batch process. This rate equation was then verified through differential and integral analysis of the experimental data. Furthermore, a substrate kinetic model for batch and continuous processes was developed. The developed rate equation will facilitate the analysis and design of sequencing batch reactor (SBR) technology for biological treatment of wastewater.

Multiway chemometric analysis of the metabolic response to toxins monitored by NMR

For the first time, the multiway chemometric model Tucker3 was applied to nuclear magnetic resonance (NMR) time series data from studies of the metabolic response to toxins. Three data sets were analysed, each consisting of 1D high-field 1H NMR spectra of rat urine samples collected over 7 days after administration of a single dose of a model toxin in different doses. The analysis using the multiway Tucker3 model resulted in models with two to three components in each mode. The observed perturbations following administration of the toxins corresponded well to those reported in other studies using two-way chemometric methods (PCA and batch processing analysis). The advantage of the Tucker3 analysis is that the three-way structure of the data (rat, time, reduced NMR spectra) is retained and used throughout the analysis. Only the metabolic perturbations that have common time profiles are modelled, whereas random or less general changes in metabolite levels, which do not have common time profiles, are considered as noise. In the Tucker3 model, no constraints are imposed on time profiles, and thus, the models can handle effects with complex time evolution behavior.

Cycle Time and Throughput Analysis in Batch Biochemical and Pharmaceutical Processing

Throughput analysis of batch processes aims at identifying opportunities for increasing annual production by identifying and eliminating size and scheduling bottlenecks. Batch size and cycle time are identified as the key parameters in throughput analysis and the relationship between them is analytically investigated in the context of a single-product batch plant. A framework for performing throughput analysis is developed and demonstrated with the use of a synthetic pharmaceutical production example.

Automation of the safety analysis of batch processes based on multi-modeling approach

In this article, a strategy for the automatic hazard analysis of batch processes and the integrated systems for the automatic hazard analysis are presented. The suggested process models for hazard analysis are based on the multi-modeling approach, which improves the overall effectiveness and efficiency of the reasoning process through cooperation of multiple models. Following the multi-modeling concept, four process representation models, including operational, material, behavioral and functional knowledge bases, and four hazard inference algorithms are established. For the case study, a batch pharmaceutical process is tested against and a maloperation analysis performed. The case study, using the devised analysis system, shows more successful and systematic capture of necessary hazard information, compared with the traditional systems.

Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start‐ups and grade transitions

There has been a lot of research activity in the area of batch process analysis and monitoring for abnormal situation detection since the pioneer work of Nomikos and MacGregor [1–5]. However, some of the key ideas and the thought process that led to those first papers have been forgotten. Batch process data are dynamic data. The whole philosophy of looking at batch process data with latent variables was developed because batch process variables are both autocorrelated and cross-correlated. Statistical process control by definition checks deviations from a nominal behavior (a target). Therefore for statistical process control of batch processes we should look at deviations of process variable trajectories from their nominal trajectories and from their nominal auto/cross-correlations. An added advantage to modeling the deviations from the target trajectory is that a non-linear problem is converted to a linear one that it is easy to tackle with linear latent variable methods such as principal component analysis (PCA) and partial least squares (PLS). This paper first takes a critical look at the true nature of batch process data. The general case where variables are not present during the entire duration of the batch is addressed. It is then illustrated how proper centering (by taking the deviations from the target trajectory) can retain valuable information on auto- and cross-correlation of the process variables. This auto- and cross-correlation is only modeled with a certain types of models. Topics such as scaling and trajectory alignment are revisited and issues arising when using the indicator variable approach are addressed. The development of control charts for multiblock, multiway PCA/PLS is discussed. Practical issues related to applications in industry are addressed. Then some of the methods that have appeared in the literature are examined as to their assumptions, their advantages and disadvantages and their range of applicability. Finally the nature of transition data (start-ups, grade transitions) is discussed and issues related to aligning, centering and scaling such types of data are presented. Copyright © 2003 John Wiley & Sons, Ltd.

Batch statistical processing of 1H NMR‐derived urinary spectral data

AbstractMultivariate statistical batch processing (BP) analysis of 1H nuclear magnetic resonance (NMR) urine spectra was employed to establish time‐dependent metabolic variations in animals treated with the model hepatotoxin hydrazine. Hydrazine was administered orally to rats (at 90 mg kg−1), and urine samples were collected from dosed rats and matched control animals (n = 5 per group) at time points up to 168 h post‐dose. Urine samples were analysed via 1H NMR spectroscopy and partial least squares‐based batch processing analysis, treating each rat as an individual batch comprising a series of timed urine samples. A model defining the mean urine profile was established for the control group, and samples obtained from hydrazine‐treated animals were assessed using this model. Time‐dependent deviations from the control model were evident in all hydrazine‐treated animals, and hepatotoxicity was manifested by increased urinary excretion of taurine, creatine, 2‐aminoadipate, citrulline and β‐alanine together with depletion of urinary levels of citrate, succinate and hippurate. The experiment was repeated at six different pharmaceutical centres in order to assess the robustness of the technology and to establish the natural variability in the data. Results were consistent across the data for all centres. BP plots showed a characteristic pattern for each toxin, allowing the time points at which there were maximum metabolic differences to be determined and providing a means of visualizing the net toxin‐induced metabolic movement of urinary metabolism. BP may prove to be a powerful metabonomic tool in defining time‐dependent metabolic consequences of toxicity and is an efficient means of visualizing inter‐animal variations in response as well as defining multivariate statistical limits of normality in terms of biofluid composition. Copyright © 2002 John Wiley & Sons, Ltd.

Metabolic trajectory characterisation of xenobiotic-induced hepatotoxic lesions using statistical batch processing of NMR data.

Multivariate statistical batch processing (BP) analysis of 1H NMR urine spectra was employed to establish time-dependent metabolic variations in animals treated with the model hepatotoxin, alpha-naphthylisothiocyanate (ANIT). ANIT (100 mg kg(-1)) was administered orally to rats (n = 5) and urine samples were collected from dosed and matching control rats at time-points up to 168 h post-dose. Urine samples were measured via 1H NMR spectroscopy and partial least squares (PLS) based batch processing analysis was used to interpret the spectral data, treating each rat as an individual batch comprising a series of timed urine samples. A model defining the mean urine profile over the 7 day study period was established, together with model confidence limits (+/-3 standard deviation), for the control group. Samples obtained from ANIT treated animals were evaluated using the control model. Time-dependent deviations from the control model were evident in all ANIT treated animals consisting of glycosuria, bile aciduria, an initial decrease in taurine levels followed by taurinuria and a reduction of tricarboxylic acid cycle intermediate excretion. BP provided an efficient means of visualising the biochemical response to ANIT in terms of both inter-animal variation and net variation in metabolite excretion profiles. BP also allowed multivariate statistical limits for normality to be established and provided a template for defining the sequence of time-dependent metabolic consequences of toxicity in NMR based metabonomic studies.