Batch Processing Systems Research Articles

Pemodelan Dinamis Proses Produksi Batch Satu Tahap Dalam Satu Mesin Dengan Memperhatikan Waktu Loading dan Waktu Proses

In the batch process, the company uses the same process facilities to produce various types of products. This allows businesses to change production in response to changes in market demand. Batch processes are widely used in industry. In many cases, batch processes provide several benefits over continuous processes, especially when the industry wants to ensure and maintain the quality standards of its products. This paper proposes a behavioral model of a single-stage batch processing system, using the language of system dynamics. When run, the behavioral model will provide an overview of how the system behaves. The system dynamics language is a formulation of a system of ordinary differential equations that is extracted from the real system being studied, in the form of a stock and flow diagram. The computation of this diagram is further implemented using software. Because it is actually a calculus method, the system dynamics language provides output data much faster than discrete simulation modeling. In this paper, the system behavior modeled is mainly loading, processing and unloading activities. The verification results carried out by testing it on two different case examples show that the model can sufficiently describe the behavior of the conceptual system logically so that the model can enrich experimental tool options related to batch production processes in the future.

Materials Properties vs. Device Performance: Batch Testing of MnOx-Loaded Carbon Nanofoam Papers for Low-Energy Faradaic Deionization

Electrochemical desalination is presently a popular topic in the scientific literature as a response to global challenges with production of potable water. Many of these reports focus on materials/electrode properties for ion capture yet fail to account for other factors that impact practical device performance. For example, gravimetric ion-storage capacity is commonly reported while areal and volumetric capacities may be more important for desalination cell performance. Furthermore, the dynamic fluid mechanics of flow-cell devices require deliberately engineered electrodes in contrast to their static-electrolyte analogs for energy-storage devices (batteries or supercapacitors). To break from the academic status quo, we have designed a computer-controlled batch-process system to investigate desalination performance of practical electrode materials and architectures. This system allows continuous desalination to high degrees of salt removal on a laboratory scale without the need to use large-footprint electrodes. We demonstrate the characteristics of materials-based vs. system-based desalination metrics using NRL-pioneered electrodes — MnOx-decorated carbon nanofoam paper (MnOx@CNFP)1 — relatives of which are effective for faradaic desalination via Na+ capture.2 Scalable and freestanding CNFP electrodes possess three-dimensionally interconnected pore structures with tunable porosities to facilitate ion transport,3 while the interior surfaces of the CNFP (>200 m2 g–1 in mesopores and macropores) are readily functionalized with nanoscale MnOx by self-limiting electroless deposition.1 We investigate the effect of MnOx@CNFP pore structure and electrode thickness on practical desalination performance, and demonstrate the importance of reporting throughput (L/m2/h) and energy consumption (Wh/L) to better validate new electrode materials and architectures in flow-cell desalination devices. E. Fischer, K. A. Pettigrew, D. R. Rolison, R. M. Stroud, and J. W. Long, Nano Lett., 2007, 7, 281–286.Hand and R. D. Cusick, Environ. Sci. Technol., 2017, 51, 20, 12027–12034.C. Lytle, J. M. Wallace, M. B. Sassin, A. J. Barrow, J. W. Long, J. L. Dysart, C. H Renninger, M. P. Saunders, N. L. Brandell, and D. R. Rolison, Energy Environ. Sci., 2011, 4, 1913–1925.

A Multichannel Fluorescence Isothermal Amplification Device with Integrated Internet of Medical Things for Rapid Sensing of Pathogens through Deep Learning.

The landscape of diagnostic assessments has experienced a paradigm shift driven by the advent of isothermal amplification techniques on point-of-care testing (POCT). The development of compact, portable isothermal amplification devices further emphasizes their transformative influence on diagnostic approaches. However, in prioritizing portability, these devices may exhibit limitations in functionality, rendering them less effective in addressing urgent public health emergencies during sudden pathogen outbreaks. In this paper, an efficient isothermal fluorescence amplification device has been fabricated for the rapid detection of pathogens during public health crises. The device features multichannel capability for simultaneous detection of various targets, integrates with the Internet of Medical Things (IoMT) for remote control and data uploading, and includes a deep learning-based batch processing system for rapid (9.4 ms) and accurate discrimination of pathogen type with excellent accuracy. The device has been successfully employed to simultaneously detect Staphylococcus aureus (SA) and methicillin-resistant Staphylococcus aureus (MRSA) with limits of detection (LODs) of 18 CFU/mL (SA) and 20 CFU/mL (MRSA) within 35 min by multiplex RPA assay and CRISPR/Cas12a-mediated nucleic acid detection assay.

Interminable Flows: A Generic, Joint, Customizable Resiliency Model for Big-Data Streaming Platforms

The examiner of cloud computing systems in the last few years observes that there is a trend of the emergence of a new Big Data framework almost every year. Since Hadoop was developed in 2007, new frameworks followed it such as Spark, Storm, Heron, Apex, Flink, Samza, Kafka...etc. Each framework is developed in a certain way to target and achieve certain objectives better than other frameworks do. However, there are few common functionalities and aspects that are shared between these frameworks. One vital aspect all these frameworks strive to achieve is better reliability and faster recovery time in case of failures. This is particularly crucial for streaming systems (compared to batch processing systems) where events are processed and monitored online in real time, and any delay in data delivery will cause a major inconvenience to the users. Another observation is that some reliability implementations are redundant between different frameworks. Encapsulating these implementations into one layer and make it shared between different applications will benefit more than one framework without the burden of re-implementing the same reliability approach in each single framework. These observations motivated us to present Warden, a generic, multi-framework, flexible, customizable, low overhead protocol to ensure the resiliency of streaming applications running on streaming Big Data frameworks. Most reliability protocols carry out one rigid fault tolerance approach targeted towards one system at a time. It is more challenging to provide a reliability approach that is pluggable in multiple Big Data frameworks at a time and can achieve low overheads comparable with single targeted framework approaches, yet is flexible and customizable by its users to make it tailored towards their objectives. The genericity is attained by providing an interface that can be used in different applications from different frameworks. The low overhead is achieved by providing faster application finish times with and without failures. The customizability is fulfilled by providing the users the options to choose between two delivery semantics (Exactly Once / At Most Once) combined with two fault tolerance guarantees (Crash Failures / Byzantine Failures). To the best of our knowledge, such approach was never tried on multiple streaming frameworks before. We built a prototype of Warden on Flink and Samza (with Kafka) streaming frameworks. Our evaluations highlight the effectiveness of our approach in the presence of failures and without failures compared to other fault tolerance techniques (such as checkpointing).

Multi-Query Optimization Revisited: A Full-Query Algebraic Method.

Sharing data and computation among concurrent queries has been an active research topic in database systems. While work in this area developed algorithms and systems that are shown to be effective, there is a lack of logical foundation for query processing and optimization. In this paper, we present PsiDB, a system model for processing a large number of database queries in a batch. The key idea is to generate a single query expression that returns a global relation containing all the data needed for individual queries. For that, we propose the use of a type of relational operators called -operators in combining the individual queries into the global expression. We tackle the algebraic optimization problem in PsiDB by developing equivalence rules to transform concurrent queries with the purpose of revealing query optimization opportunities. Centering around the -operator, our rules not only cover many optimization techniques adopted in existing batch processing systems, but also revealed new optimization opportunities. Experiments conducted on an early prototype of PsiDB show a performance improvement of up to 36X over a mainstream commercial DBMS.

Migration of Batch Processing Systems in Financial Sectors to Near Real-Time Processing

Migration of Batch Processing Systems in Financial Sectors to Near Real-Time Processing

A Survey on IoT Big Data Analytic Systems: Current and Future

The Internet of Things (IoT) has become widespread around the world. Since a large number of diverse devices, such as vehicles, household electrical appliances, smart phones, and environmental sensors are connected to the Internet, we can obtain a large volume of diverse IoT data, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoT big data</i> . The generation of IoT big data means that efficient analytic systems are needed for many application scenarios, for example, to optimize urban planning, solve air pollution problems, and improve business decisions. In this survey, we review current systems that can efficiently analyze IoT data. Existing systems can be categorized into batch and stream processing systems. We explore Hadoop- and Spark-based batch processing systems for spatiotemporal and trajectory data. We also review fog- and edge-aware stream processing systems. Although many existing systems can efficiently and effectively analyze specific data and tasks, no system exists that can handle all characteristics of IoT big data: volume, velocity, variety, veracity, and variability. We present some open issues and discuss the future of IoT big data analytic systems. This survey aims to help researchers and practitioners better understand current systems and develop new IoT big data analytic systems.

Fault Diagnosis System of Power Grid Based on Multi-Data Sources

In order to complete the function of power grid fault diagnosis accurately, rapidly and comprehensively, the power grid fault diagnosis system based on multi-data sources is proposed. The integrated system uses accident-level information, warning-level information and fault recording documents and outputs a complete diagnosis and tracking report. According to the timeliness of three types of information transmission, the system is divided into three subsystems: real-time processing system, quasi-real-time processing system and batch processing system. The complete work is realized through the cooperation between them. While a real-time processing system completes fault diagnosis of elements, it also screens out incorrectly operating protections and circuit breakers and judges the loss of accident-level information. Quasi-real-time system outputs reasons for incorrect actions of protections and circuit breakers under the premise of considering partial warning-level information missing. The batch processing system corrects diagnosis results of the real-time processing system and outputs fault details, including fault phases, types, times and locations of faulty elements. The simulation results and test show that the system can meet actual engineering requirements in terms of execution efficiency and fault diagnosis and tracking effect. It can be used as a reference for self-healing and maintenance of power grids and has a preferable application value.

Intelligent sensitivity analysis framework based on machine learning for spacecraft thermal design

The variance-based method of global sensitivity analysis (GSA) has been widely applied in spacecraft thermal design, which is typically calculated using Monte Carlo estimations. However, such estimations require a large number of samples to ensure sufficient accuracy, which makes GSA expensive to perform when modeling is difficult. Moreover, multimodal or highly skewed output distributions may result in the use of variance as an uncertain agent that generates contradictory results. Therefore, an intelligent density-based GSA framework based on machine learning and multi-fidelity metamodels called IDGSA-3M is proposed. An intelligent batch processing system based on a real-time data interaction between MATLAB and NX/TMG was designed that uses many cheap low-fidelity sample points to reduce the cost of model evaluation while using a small number of expensive high-fidelity sample points to maintain high accuracy, thus achieving trade-offs between high accuracy and low computational cost. A radial basis function (RBF) neural network based on an improved mind evolutionary algorithm was applied to approximate the multi-fidelity metamodel of a spacecraft thermophysical model calculated using a batch processing system, which had a computational speed that was 1000+ times faster than that of the traditional thermophysical model and a high computational accuracy of 99%+. The output distributions of the RBF were then characterized by its cumulative distribution functions to obtain density-based sensitivity indices. Both the theoretical and experimental results of GSA for the thermal design parameters of the extreme ultraviolet radiation detector on the space-based Lyman-Alpha Solar Telescope, developed in China, demonstrated that the convergence rate of IDGSA-3M can be improved up to 10-fold for a fixed convergence level in comparison with two other GSA methods, thereby verifying its superiority.

MIRAGE: A consolidation aware migration avoidance genetic job scheduling algorithm for virtualized data centers

Modern virtualized data centers often rely on virtual machine (VM) migrations to consolidate workload on a single machine for energy saving. But VM migrations have many drawbacks, including performance degradation, service disruption etc. Hence, many approaches have been proposed to minimize the overhead when migrations occur. In contrast, this work aims to proactively avoid migrations from happening in the first place. We have proposed a novel consolidation aware scheduling algorithm to minimize the number of migrations for batch processing systems by taking advantage of the prior knowledge of consolidation strategy and job information. We show the problem can be formulated as an integer linear programming (ILP) problem, and an effective heuristic solution can be found by a genetic algorithm. Both real and synthetic workload traces were used to evaluate our methods. Experimental results showed that, after comparing with two popular job scheduling algorithms, our approach has reduced the number of migrations by more than 25%.

Design locomotion for automatic guided vehicles using double ackerman inverted method (2WD-4WS-1A)

This paper introduces a robot vehicle designed for the same forward or reverse maneuver specifications. The vehicle has two two-wheel drives with a Double Ackermann Inverted (2WD-4WS-1A) steering method for Automatic Guide Vehicle (AGV). In the batch processing system industrial estate, AGV is used for transportation in the component warehouse area. In the steering system, Ackermann’s mechanics are described as optimizing the maximum maneuvering radius of the AGV, based on the turning center point. AGV drive is designed with four wheels, two drives and one steering mechanics. The vehicle’s mechanical steering system is designed with a system geometry analysis to control the turning radius of the vehicle. The Ackermann AGV steering method that has been implemented will be tested for its maneuvers. The results of the study were measured empirically to determine the efficiency and optimization of the system in the AGV design. The efficiency of steering control using only 1 steering wheel as 4 wheel control and small optimal radius on the AGV will increase maneuverability compared to using 2 WD.

Viscosity Variation of Model Compounds during Hydrothermal Liquefaction under Subcritical Conditions of Water

Recent assessments in waste-to-energy technologies highlight hydrothermal liquefaction (HTL) as a suitable process for converting organic-rich waste with high moisture content into a useful resource. Organic waste materials, including sewage sludge, food, and agricultural waste residues, typically contain lipids, carbohydrates, proteins, and lignin with varying compositions depending on their origin. Fluid properties of reacting HTL slurries under subcritical water conditions, particularly viscosity and density, affect material flow and heat transfer in both batch and continuous HTL process systems. Real-time viscosity variations of 20 wt % water slurries of model compounds, sunflower oil, sucrose, and soy protein were determined in a stirred tank batch reactor using the Metzner–Otto method. Measured torque on the impeller shaft at a fixed impeller speed was used to determine the changes in the viscosity of the reacting slurry at different reaction temperatures. Changes in the viscosity of the sunflower oil mixture were insignificant as compared to viscosity variations with sugar- and soy protein-derived feedstock. The viscosity of the soy protein solution decreased rapidly with the temperature during hydrolysis of polypeptides into amino acids between 25 and 100 °C. Further increase in the temperature led to minimal changes in viscosity as more soluble compounds were produced above 100 °C. The viscosity of the sucrose solution changed significantly above 250 °C when carbon compounds precipitated from the solution. Viscosity variations of mixtures of model compounds were determined to predict viscosity changes in biomass.

Data-driven anomaly detection and diagnostics for changeover processes in biopharmaceutical drug product manufacturing

A two-step approach is presented for anomaly detection and diagnosis in batch process systems. The approach was applied to a case study of a decontamination process in biopharmaceutical drug product manufacturing. Analysis of historical operation data from four consecutive years was combined with process knowledge to achieve a deeper understanding of process failures, their impacts, and precursors. For anomaly detection, failure boundaries were established within the data space for different types of errors and process steps. The failure boundaries helped in both the detection and diagnosis of faults as each fault occupied unique boundaries. Precursors were identified as data from “non-failed” runs start venturing into the failure boundaries. The use of a rolling window enabled the differentiation of shifts due to failure in operations from shifts due to regular maintenance and calibration works. For diagnosis, a root cause analysis was used to identify irregularities in “non-failed” runs and to detect underlying equipment problems requiring preemptive intervention. Overall, the approach successfully addressed challenges in anomaly detection for batch runs, including drifting, overlapping, and imbalanced data. The integration of process understanding into the data analysis played an important role in bridging the information gaps. The gained insights are valuable for operation support, leading to a reduction in downtime and higher production efficiency. The approach can be used to help identifying sources of failures and the required corrective actions as an intermediate-term goal between fault detection and predictive maintenance. The proposed approach is robust and could be extended to other changeover and production operations.

Udayana University International Student Management: A Business Process Reengineering Approach

The research had two aims. First, it presented an overview and progress in understanding the current business process in the international program of Udayana University, which had a long queue for processing student admission documents. Second, it described how Business Process Reengineering (BPR) helped to improve the educational management services. Multiple interviews and a Forum Group Discussion (FGD) with the stakeholders were conducted. The reviews of the available documents, the current business process, and related complaint records were also carefully performed. The results show that the currently running batch processing system is a major challenge. The proposed solution reengineers the current business process and implements an information technology tool as a driver by digitizing the proposed solutions. The new business process leads to an improvement in processing the international student application from more than 60 days to only 29 days. Moreover, the proposed business process can minimize the opportunity loss of more than Rp3,8 billion annually.

GRID System Based on European EGI Standards for Large-Scale Calculations by the Original Accelerated Method of Quantum Chemistry

Based on the analysis of modern tools for the creation of GRID-type information systems incorporated into the UMD repository, which has become a European EGI “standard” (including new version of Globus Toolkit, ARC, dCache, etc.), the application of GRID systems for computational chemistry problems is briefly considered. The GRID system created by the authors is a combination of two Linux CentOS 7 clusters and based on the UMD-4 software. The relevance and efficiency of the application of batch processing systems (we use Torque 4.2.10) in quantum-chemical calculations are increased in the mass calculations of docking complexes (including drug design problems) and, for these purposes, an improved semiempirical method implemented with more efficient approximations in the LSSDOCL software in Fortran-95 has been proposed. New methods of approximation, including the methods for DFT functionals, have been developed for such calculations to perform their software implementation. The convertors of LSSDOCK calculation results into the XML based CML version 3 format, which is more natural for GRID, have been developed. Based on the CML format and the dCache software tools, the universal tree of a virtual GRID file system distributed between heterogeneous nodes for the storage of LSSDOCK calculation results has been implemented.

Minimizing Freshwater Usage in Batch Process Scheduling: S-Graph Approach

Water is one of the most important natural resources of life. While it is considerably cheap and vastly available currently, except for extreme locations, this is not guaranteed in the future. Being provident with water has multitude of advantages in both short and long term. Using less clean water not only brings immediate financial benefits, it simultaneously reduces wastewater production, related treatment costs, and the impact on the environment. Reducing the water footprint of a batch system is not a trivial task, as water sources and sinks need to be matched not only in quantity and quality, but in time as well. In this paper, the S-graph scheduling framework is extended to address simultaneous scheduling and water minimization in batch processing systems. The proposed approach tackles truly batch processes with a single contaminant, and allows only a single water source to be reused for each sink. The presented algorithm and S-graph extension have been implemented and tested on various case studies from the literature. The results of this paper provide an opportunity for further extensions to address a wider range of problems with multiple contaminants, semi-continuous behavior, cyclic operations, etc.

A Spatiotemporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems.

Anomaly detection is a critical task for maintaining the performance of a cloud system. Using data-driven methods to address this issue is the mainstream in recent years. However, due to the lack of labeled data for training in practice, it is necessary to enable an anomaly detection model trained on contaminated data in an unsupervised way. Besides, with the increasing complexity of cloud systems, effectively organizing data collected from a wide range of components of a system and modeling spatiotemporal dependence among them become a challenge. In this article, we propose TopoMAD, a stochastic seq2seq model which can robustly model spatial and temporal dependence among contaminated data. We include system topological information to organize metrics from different components and apply sliding windows over metrics collected continuously to capture the temporal dependence. We extract spatial features with the help of graph neural networks and temporal features with long short-term memory networks. Moreover, we develop our model based on variational auto-encoder, enabling it to work well robustly even when trained on contaminated data. Our approach is validated on the run-time performance data collected from two representative cloud systems, namely, a big data batch processing system and a microservice-based transaction processing system. The experimental results show that TopoMAD outperforms some state-of-the-art methods on these two data sets.

On the Throughput Optimization in Large-scale Batch-processing Systems

We analyse a data-processing system with n clients producing jobs which are processed in batches by m parallel servers; the system throughput critically depends on the batch size and a corresponding sub-additive speedup function. In practice, throughput optimization relies on numerical searches for the optimal batch size, a process that can take up to multiple days in existing commercial systems. In this paper, we model the system in terms of a closed queueing network; a standard Markovian analysis yields the optimal throughput in ωn4 time. Our main contribution is a mean-field model of the system for the regime where the system size is large. We show that the mean-field model has a unique, globally attractive stationary point which can be found in closed form and which characterizes the asymptotic throughput of the system as a function of the batch size. Using this expression we find the asymptotically optimal throughput in O(1) time. Numerical settings from a large commercial system reveal that this asymptotic optimum is accurate in practical finite regimes.

Enhancing the energetic potential of Mediterranean food waste by anaerobic co‐digestion with sewage sludge

AbstractMediterranean Food Waste (FW) with high Carbon/Nitrogen (C/N) ratio (≈31) and a biodegradability (BD) above 75%, were anaerobically co‐digested with Sewage Sludge (SS), in both batch and semicontinuous processing systems. Biochemical Methane Potential (BMP) tests were carried out for five mixture ratios (VS basis). The highest methane yield was obtained for the Food Waste to Sewage Sludge (FW/SS) ratio of 3:1, reaching a Specific Methane Production (SMP) of of 452 ml CH4/g VSadded. Moreover, this ratio has shown a BD of 40%, twofold lower than the one obtained for digestion of FW alone. Applying the semi‐continuous process, a methane yield of 200 ml CH4/g VS was obtained at the high Organic Loading Rate (OLR) of 8 g VS/L·day, indicating a good performance of the system facing a high daily feeding. A predictive study of the energy efficiency of this system provided an estimated daily production of 1.4 kilowatt‐hour electricity load (KWhel)/kg VSadded and 2.2 kilowatt‐hour thermal load (KWhth)/kg VSadded.

Design of secure authentication protocol for dynamic user addition in distributed Internet-of-Things

Rapid rise in adoption of smart devices in distributed Internet-of-things (IoT) has resulted in an exponential increase in volume of generated data. However, due to resource-constrained sensor nodes and vulnerable communication channels, information protection has become a key concern and prominent factor for improvement in IoT environment. Batch processing systems have several shortcomings as compared to distributed systems. Distributed systems enable dynamic changes in the potential number of sensors, actuators, RFID’s, resources, users and subjects to tolerate unbounded interruptions in communication patterns. In this paper, we address the key challenges of communication security and scalability in IoT environment. We hypothesize distributed IoT approach between non-identical entities to communicate through secure authentication protocol for dynamically adding new user with partially involved trusted third party (TTP) as our module 1 and attribute based encryption scheme for non-identical smart entities to communicate securely as our module 2. We discuss module 1 in this paper. The existing approaches that focus on fully trusted central authority having full rights lead to serious weakness in violating security rules, while multi-authority based systems display revocation and compulsory user participation issues. As ideal functionalities we view TTP as honest-but-curious, honestly following the protocol while on the other hand curious to decrypt the cipher-text to breach the intention of encrypting parties. This motivates us to design an approach which jointly achieves effective authentication and partial trust management with scalability in distributed IoT environment. In accordance, we propose a novel authentication scheme with partially involved TTP nomenclatured Secure Dynamic User Addition Protocol (SDUAP) based on JWT (JSON Web Token) challenge and response game using symmetric key cryptography. The security of SDUAP is proved under real or random oracle model and its robustness against several attacks is verified using scyther security verification tool. We also compare performance of SDUAP with relatively similar modeled schemes in terms of communication, computation and storage overheads. The purpose of this paper is to mitigate the complications associated with scalability and curious/suspicious third party in distributed IoT approaches.