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Process Modeling

Hans–Albert Rupprecht · 1977

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Process Modeling

Gary S. May Ph.D., Costas J. Spanos Ph.D. · 2006

Data derived from designed experiments can be used to construct process models of various types that enable the analysis and prediction of manufacturing process behavior. Process modeling permits an engineer to manipulate and optimize the process efficiency with a minimum amount of experimentation. A well-developed process model can be used to generate a recipe of the process deposition conditions to obtain particular desired responses. This chapter explores various process modeling methodologies, from traditional regression analysis to more contemporary AI-based approaches for deriving predictive models in semiconductor manufacturing applications. We then explore various optimization (or recipe synthesis) procedures.

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Process modeling

Bill Curtis, Marc I. Kellner, Jim Over · Communications of the ACM · 1992

raditionally, the modeling of information systems has focused on analyzing data flows and transformations. This modeling accounted only for the organization's data and that portion of its processes that interacted with data. Newer uses of information technology extend computer use beyond transaction processing into communication and coordination. Successfully integrating these systems into the enterprise often requires modeling even the manual organizational processes into which these systems intervene. The following are three such applications:

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Process Modeling

Gregory K. McMillan · 2026

Process models can be broadly categorized as steady state and dynamic.Steady-state models are largely used for process and equipment design and real-time optimization (RTO) of continuous processes.Dynamic models are used for system acceptance testing (SAT), operator training systems (OTS), and process control improvement (PCI).A steady-state or dynamic model can be experimental or first principle.Experimental models are identified from process testing.First principle models are developed using the equations for charge, energy, material, and momentum balances; equilibrium relationships; and driving forces.Steady-state models can be multivariate statistical, neural-network, or first principle models.Multivariate statistical and neural-network models are primarily used to detect abnormalities and make predictions in process operation.First principle steady-state models are widely used by process design engineers to develop process flow diagrams and, to a much lesser extent, by advanced control engineers for RTO.Multivariate statistical and neural-network dynamic models presently rely on the insertion of dead-time blocks on process inputs to model the effect on downstream process outputs.The lack of a process time constant or integrating response means that these models cannot be used for testing feedback control systems.For batch processes, the prediction of batch endpoint conditions does not require dead-time blocks because synchronization with current values is not required.Dynamic first principle models should include the dynamics of the automation system in addition to the process as shown in Figure 17-1.Valve and variable-speed drive (VSD) models should include the installed flow characteristic, resolution and sensitivity limits, deadband, dead time, and a velocity limited exponential response.Measurement models should include transportation delays, sensor lag and delay, signal filtering, transmitter damping, resolution and sensitivity limits, and update delays.For analyzers, the measurement models A Guide to the Automation Body of Knowledge.

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Process Modeling

Shercliff, HR, Colegrove, PA, , · Cambridge University Engineering Department Publications Database · 2007

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Modeling process.

Jonathan M. Dreyfuss (270407), Jeremy D. Zucker (434653), Heather M. Hood (434654) et al. · Figshare · 2015

<p>The process used for the reconstruction and validation of the metabolic model is described in four stages. In the first stage, <i>pathway-directed curation</i>, the genome sequence annotation <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003126#pcbi.1003126-Galagan1" target="_blank">[14]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003126#pcbi.1003126-Borkovich1" target="_blank">[15]</a> , metabolic pathways derived from MetaCyc <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003126#pcbi.1003126-Caspi1" target="_blank">[34]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003126#pcbi.1003126-Karp4" target="_blank">[111]</a> and experimental evidence from the <i>Neurospora</i> bibliome <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003126#pcbi.1003126-Karp1" target="_blank">[37]</a> were used to construct the first draft of the NeurosporaCyc Pathway/Genome database <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003126#pcbi.1003126-Karp5" target="_blank">[112]</a>. For the second stage, <i>iterative phenotype-directed curation</i>, we utilized FARM to suggest changes to the metabolic network based on a training set of experimentally observed growth phenotypes. These suggestions were reviewed manually, and accepted into the final model only if they were consistent with published experimental evidence. In the third stage, we <i>independently validated</i> the model based on a test set of experimentally observed viability phenotypes that were not utilized during model construction. In the fourth stage, we <i>comprehensively predicted</i> the phenotypes of all essential genes, nutrient rescues, and synthetic lethal interactions.</p>

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Modeling Process.

Mohammed Majeed Hameed (14053535), Adil Masood (14053526), Aadil hamid (21417847) et al. · Figshare · 2025

<div> Accurate monthly runoff forecasting is vital for water management, flood control, hydropower, and irrigation. In glacierized catchments affected by climate change, runoff is influenced by complex hydrological processes, making precise forecasting even more challenging. To address this, the study focuses on the Lotschental catchment in Switzerland, conducting a comprehensive comparison between deep learning and ensemble-based models. Given the significant autocorrelation in runoff time series data, which may hinder the evaluation of prediction models, a novel statistical method is employed to assess the effectiveness of forecasting models in detecting turning points in the runoff data. The performance of Extreme Gradient Boosting (XGBoost) was compared with long short-term memory (LSTM) and random forest (RF) models for one-month-ahead runoff forecasting. The study used 20 years of runoff data (2002–2021), with 70% (2002–2015) dedicated for training and calibration, and the remaining data (2016–2021) for testing. The findings for the testing phase results show that the XGBoost model achieves the best accuracy, with <i>R²</i> of 0.904, <i>RMSE</i> of 1.554 m³/sec, an <i>NSE</i> of 0.797, and Willmott index (<i>d</i>) of 0.972, outperforming both the LSTM and RF models. The study also found that the XGBoost model estimated turning points more accurately, obtaining forecasting improvements of up to 22% to 34% compared to LSTM and RF models. Overall, the study’s findings are essential for global water resource management, providing insights that can inform sustainable practices to support societies impacted by climate change. </div>

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Process Modeling

Günter Wozny, Antonis Kokossis, Patrick Linke et al. · Electrical and computer engineering · 2005

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Process Modeling

Barraud, Alain, Lesecq, Suzanne, Gentil, Sylviane · HAL (Le Centre pour la Communication Scientifique Directe) · 2007

International audience

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Modeling process.

Peiyao Shu (12215066), Chengqi Xue (18452372), Gengpei Zhang (10131572) et al. · Figshare · 2024

&lt;div&gt;&lt;p&gt;Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP columns, thereby impacting the whole project. Therefore, it is highly significant to establish the prediction models for ultimate displacement and ultimate bearing capacity to optimize the design of the two types of GFRP columns. In this study, based on the experiments conducted under different conditions on the two kinds of GFRP columns, automatic machine learning along with four other commonly used machine learning methods were employed for modeling to analyze how the column parameters (cross section shape, concrete strength, height of GFRP column, wound GFRP wall thickness, inner diameter of wound GFRP column) affect their performance. The differences in performance among these five machine learning methods were analyzed after modeling. Subsequently, we obtained the variation patterns in ultimate displacement and ultimate bearing capacity of the columns influenced by each parameter by testing the data using the optimal model. Based on these findings, the optimal design schemes for the two types of GFRP columns are proposed. The contribution of this paper is three-fold. First, AutoML sheds light on the automatic prediction of ultimate displacement and ultimate bearing capacity of GFRP column. Second, in this paper, two optimal design schemes of GFRP columns are proposed. Third, for AEC industrial practitioners, the whole process is automatic, accurate and less reliant on data expertise and the optimization design scheme proposed in the article is relatively scientific.&lt;/p&gt;&lt;/div&gt;

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Process Modeling

Anna Kosieradzka, Katarzyna Rostek · RePEc: Research Papers in Economics

Abstract Process Modeling is the first stage of the process lifecycle. It entails identification, definition, and design of processes, as well as introduction of changes in already existing processes (redesign). Said elements are characterized and described in the context of two distinct types of organizations—production companies and public administration institutions. The significance of the organization type in the context of process modeling is demonstrated already in the first subchapter under each of the identified aspects—the organizational context. It allows one to understand the deliberations and the methodologies described in the subsequent subchapters. The identified similarities and differences are gathered and discussed in the chapter’s summary along with the indications and recommendations for the dedicated process modelling approach in respective types of organizations.

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Process modeling

Joppich, W., Mijalkovic, S. · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · 1999

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Process modeling

Zehra Cagnan · 1990

In this study, a simulation-based methodology is being developed to model post-earthquake restoration processes of lifelines as part of the MCEER research on Los Angeles Department of Water and Power’s (LADWP’s) electric and water supply systems. Post-earthquake restoration models play an important role in estimating the economic impact of earthquake damage to lifeline systems. This paper begins with a discussion of the available restoration modeling approaches, listing advantages and disadvantages of each. The new simulation-based methodology is described, and key innovations that distinguish it from previous approaches are explained. Finally, plans for possible future extensions are discussed.

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Process Modeling

Stephen M. Hall · Elsevier eBooks · 2018

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Modeling process.

Tianyi Deng (14000838), Chengqi Xue (18452372), Gengpei Zhang (10131572) · Figshare · 2024

&lt;div&gt;&lt;p&gt;The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with optimization-based data augmentation from meta-learning, the proposed deep neural network demonstrates excellent performance in optimizing glass fiber reinforced plastic (GFRP) for wrapping concrete short columns. When compared with traditional regression models, such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Radial Basis Function Neural Networks (RBFNN), the meta-learning method proposed here performs better in modeling small data samples. The success of this approach illustrates the potential of deep learning in dealing with limited amounts of data, offering new opportunities in the field of material data analysis.&lt;/p&gt;&lt;/div&gt;

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Process modeling

Joppich, W., Mijalkovic, S. · Fraunhofer-Publica (Fraunhofer-Gesellschaft) · 1999

S.127-135

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Process Modeling

Chitra Sharma · RePEc: Research Papers in Economics

Abstract Process modeling is a very important technique of the tools and technique “tan.” Business process transformation requires a clear visualization of the proposed process coupled with insights from the current process. Process modeling enables achieving the same. This chapter attempts to explain how the “as is” process and “to be” process can be modeled. In order to facilitate understanding, the important conceptual frameworks are discussed. Once it is known, what is to be modeled, business process modeling and notation come into picture. This chapter explains process modeling with BPMN2.0 and provides guidelines for documentation.

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Information Modeling and Process Modeling

Hans-Jürgen Scheruhn, Mark von Rosing, Richard L. Fallon · Elsevier eBooks · 2014

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Business Process Management - Process Modeling

Tomáš Kupka · Digital Repository (National Repository of Grey Literature) · 2015

This diploma thesis deals with the topic of Business Process Management, its development and fundamental paradigms in the field of BPM as BPR. The thesis also compares approaches to BPM. Furthermore, it defines the term "process" and describes its classification. The thesis also explains the concept of the process-driven organization, including its characteristics. An integral part of the thesis is the explanation of the holistic approach to BPM and its technical aspect, i.e. the business process modeling which deals with creating the diagrams classified by basic principles of object orientation. The thesis introduces general modeling language UML and its specification in the form of notation BPMN and Eriksson-Penker. Also there are shown few examples of diagrams used in the BPM. All of these findings are then used in the constructing of process models and diagrams in the defined research area of the particular organization.

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PROCESS MODELING IN PULPING PROCESS

Huizhong Zhang · Theseus (Ammattikorkeakoulujen) · 2010

The purpose of this bachelor’s thesis is to know well the function and importance of process modeling in regards to an industry project, from the preliminary process design until shutting the plant down. A variety of software simulators in present are employed in process modeling, thus an additional purpose is get familiar with the application of one simulator. BALAS simulator is selected and offered by Saimaa University of Applied Sciences, Imatra. The results with the BALAS simulator are done on sources of variation in mechanical and chemimechanical pulping processes. &#13;\nIn the simulation part, PGW 70 and CTMP pulping process were employed. Various wood raw materials should effect the pulp production and consumptions of all kinds of raw material. The comparing analysis consists of the consumption of wood, chemicals, water, and energy by two processes, the proportions between the amount of water circulation and energy recovery and the amount of using water and energy. Though, the BALAS simulator is focuses on preliminary process design and operation. More accurate results cannot be obtained if process simulated with only one single simulator.

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