scholarly journals Student’s-t Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3968 ◽  
Author(s):  
Jingbo Wang ◽  
Weiming Shao ◽  
Zhihuan Song
Keyword(s):  
Learning Algorithm ◽  
Real Life ◽  
Industrial Process ◽  
Soft Sensor ◽  
Operating Conditions ◽  
Industrial Processes ◽  
Computationally Efficient ◽  
Mixture Regression ◽  
Student’S T ◽  
T Distribution

Because of multiple manufacturing phases or operating conditions, a great many industrial processes work with multiple modes. In addition, it is inevitable that some measurements of industrial variables obtained through hardware sensors are incorrectly observed, recorded or imported into databases, resulting in the dataset available for statistic analysis being contaminated by outliers. Unfortunately, these outliers are difficult to recognize and remove completely. These process characteristics and dataset imperfections impose challenges on developing high-accuracy soft sensors. To resolve this problem, the Student’s-t mixture regression (SMR) is proposed to develop a robust soft sensor for multimode industrial processes. In the SMR, for each mixing component, the Student’s-t distribution is used instead of the Gaussian distribution to model secondary variables, and the functional relationship between secondary and primary variables is explicitly considered. Based on the model structure of the SMR, a computationally efficient parameter-learning algorithm is also developed for SMR. Results conducted on two cases including a numerical example and a real-life industrial process demonstrate the effectiveness and feasibility of the proposed approach.

Dynamic Positioning System: Systematic Weight Assignment for DP Sub-Systems Using Multi-Criteria Evaluation Technique Analytic Hierarchy Process and Validation Using DP-RI Tool With Deep Learning Algorithm

2019 ◽  
Author(s):  
Charles Fernandez ◽  
Arun Kr. Dev ◽  
Rose Norman ◽  
Wai Lok Woo ◽  
Shashi Bhushan Kumar
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Learning Algorithm ◽  
Real Life ◽  
Operating Conditions ◽  
Dynamic Positioning ◽  
Analytic Hierarchy ◽  
Dynamic Positioning System ◽  
Weight Assignment ◽  
Hierarchy Process

Abstract The Dynamic Positioning (DP) System of a vessel involves complex interactions between a large number of sub-systems. Each sub-system plays a unique role in the continuous overall DP function for safe and reliable operation of the vessel. Rating the significance or assigning weightings to the DP sub-systems in different operating conditions is a complex task that requires input from many stakeholders. The weighting assignment is a critical step in determining the reliability of the DP system during complex marine and offshore operations. Thus, an accurate weighting assignment is crucial as it, in turn, influences the decision-making of the operator concerning the DP system functionality execution. Often DP operators prefer to rely on intuition in assigning the weightings. However, it introduces an inherent uncertainty and level of inconsistency in the decision making. The systematic assignment of weightings requires a clear definition of criteria and objectives and data collection with the DP system operating continuously in different environmental conditions. The sub-systems of the overall DP system are characterized by multi-attributes resulting in a high number of comparisons thereby making weighting distribution complicated. If the weighting distribution was performed by simplifying the attributes, making the decision by excluding part of them or compromising the cognitive efforts, then this could lead to inaccurate decision making. Multi-Criteria Decision Making (MCDM) methods have evolved over several decades and have been used in various applications within the Maritime and Oil and Gas industries. DP, being a complex system, naturally lends itself to the implementation of MCDM techniques to assign weight distribution among its sub-systems. In this paper, the Analytic Hierarchy Process (AHP) methodology is used for weight assignment among the DP sub-systems. An AHP model is effective in obtaining the domain knowledge from numerous experts and representing knowledge-guided indexing. The approach involved examination of several criteria in terms of both quantitative and qualitative variables. A state-of-the-art advisory decision-making tool, Dynamic Positioning Reliability Index (DP-RI), is used to validate the results from AHP. The weighting assignments from AHP are close to the reality and verified using the tool through real-life scenarios.

Modelling Gas Processing Unit: An Inter-disciplinary Approach Based on Petri Nets

2008 ◽  
Vol 59 (7) ◽  
Author(s):  
Sanda Florentina Mihalache
Keyword(s):  
Industrial Process ◽  
Industrial Processes ◽  
Processing Unit ◽  
Industrial System ◽  
Modeling Framework ◽  
Gas Processing ◽  
Human Interactions ◽  
Modelling Method ◽  
Effective Representation ◽  
Modelling Approach

A modelling approach that will facilitate an in-depth understanding of the interactions of the different phenomena, human interactions and environmental factors constituting �real world� industrial processes is presented. An important industrial system such as Gas Processing Unit (GPU) have inter-related internal process activities coexisting with external events and requires a real time inter-disciplinary approach to model them. This modeling framework is based on identifying as modules, the part of processes that have interactions and can be considered active participants in overall behaviour. The selected initial set of modules are structured as Petri net models and made to interact iteratively to provide process states of the system. The modeling goal is accomplished by identifying the evolution of the process states as a means of effective representation of the �actual running�� of the industrial process. The paper discusses the function and the implementation of the modelling method as applicable to the industrial case of GPU.

scholarly journals Estimating Simultaneous Equation Models through an Entropy-Based Incremental Variational Bayes Learning Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 384
Author(s):  
Rocío Hernández-Sanjaime ◽  
Martín González ◽  
Antonio Peñalver ◽  
Jose J. López-Espín
Keyword(s):  
Statistical Theory ◽  
Learning Algorithm ◽  
Real Life ◽  
Simulated Data ◽  
Simultaneous Equation ◽  
Variational Bayes ◽  
Parameter Estimates ◽  
Step Method ◽  
The One ◽  
Simultaneous Equation Models

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.

scholarly journals Modeling of a Real-Life Industrial Reactor for Hydrogenation of Benzene Process

Catalysts ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 412
Author(s):  
Mirosław K. Szukiewicz ◽  
Krzysztof Kaczmarski
Keyword(s):  
Catalyst Activity ◽  
Real Life ◽  
Operating Conditions ◽  
Reactor Operation ◽  
Reactor Model ◽  
High Scale ◽  
Industrial Reactor ◽  
Knowledge Based ◽  
Operation Conditions ◽  
Insight Into

A dynamic model of the hydrogenation of benzene to cyclohexane reaction in a real-life industrial reactor is elaborated. Transformations of the model leading to satisfactory results are presented and discussed. Operating conditions accepted in the simulations are identical to those observed in the chemical plant. Under those conditions, some components of the reaction mixture vanish, and the diffusion coefficients of the components vary along the reactor (they are strongly concentration-dependent). We came up with a final reactor model predicting with reasonable accuracy the reaction mixture’s outlet composition and temperature profile throughout the process. Additionally, the model enables the anticipation of catalyst activity and the remaining deactivated catalyst lifetime. Conclusions concerning reactor operation conditions resulting from the simulations are presented as well. Since the model provides deep insight into the process of simulating, it allows us to make knowledge-based decisions. It should be pointed out that improvements in the process run, related to operating conditions, or catalyst application, or both on account of the high scale of the process and its expected growth, will remarkably influence both the profits and environmental protection.

A Solution Framework for Response Surface-Based Multiple Quality Characteristics Optimization

2018 ◽  
Vol 25 (05) ◽  
pp. 1850025
Author(s):  
Sasadhar Bera ◽  
Indrajit Mukherjee
Keyword(s):  
Response Surface ◽  
Optimization Problems ◽  
Real Life ◽  
Quality Characteristics ◽  
Operating Conditions ◽  
Simultaneous Optimization ◽  
Trade Off ◽  
Multiple Quality Characteristics ◽  
Pin On Disc ◽  
Application Potential

A common problem generally encountered during manufacturing process improvement involves simultaneous optimization of multiple ‘quality characteristics’ or so-called ‘responses’ and determining the best process operating conditions. Such a problem is also referred to as ‘multiple response optimization (MRO) problem’. The presence of interaction between the responses calls for trade-off solution. The term ‘trade-off’ is an explicit compromised solution considering the bias and variability of the responses around the specified targets. The global exact solution in such types of nonlinear optimization problems is usually unknown, and various trade-off solution approaches (based on process response surface (RS) models or without using process RS models) had been proposed by researchers over the years. Considering the prevalent and preferred solution approaches, the scope of this paper is limited to RS-based solution approaches and similar closely related solution framework for MRO problems. This paper contributes by providing a detailed step-by-step RS-based MRO solution framework. The applicability and steps of the solution framework are also illustrated using a real life in-house pin-on-disc design of experiment study. A critical review on solution approaches with details on inherent characteristic features, assumptions, limitations, application potential in manufacturing and selection norms (indicative of the application potential) of suggested techniques/methods to be adopted for implementation of framework is also provided. To instigate research in this field, scopes for future work are also highlighted at the end.

Experimental study of mild steel cutting process by using the plasma arc method

2021 ◽  
Vol 108 (2) ◽  
pp. 75-85
Author(s):  
H.M. Magid
Keyword(s):  
Industrial Process ◽  
Scanning Speed ◽  
Cutting Parameters ◽  
Operating Conditions ◽  
Kerf Width ◽  
Cutting Process ◽  
Plasma Cutting ◽  
Plasma Arc ◽  
Cutting Equipment

Purpose: In this study, plasma arc cutting (PAC) is an industrial process widely used for cutting various away types of metals in several operating conditions. Design/methodology/approach: It is carried out a systematic or an authoritative inquiry to discover and examine the fact, the plasma cutting process is to establish the accuracy and the quality of the cut in this current paper assessed a good away to better the cutting process. Findings: It found that the effect of parameters on the cutting quality than on the results performed to accomplish by statistical analysis. Research limitations/implications: The objective of the present work paper is to achieve cutting parameters, thus the quality of the cutting process depends upon the plasma gas pressure, scanning speed, cutting power, and cutting height. Practical implications: The product of the plasma cutting process experimentally has been the quality of the cutting equipment that was installed to monitor kerf width quality by exam the edge roughness, kerf width, and the size of the heat-affected zone (HAZ). Originality/value: The results reveal that were technically possessed of including all the relevant characteristics, then a quality control for the cutting and describe the consequence of the process parameters.

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