scholarly journals Improved Understanding of Industrial Process Relationships Through Conditional Path Modelling With Process PLS

2021 ◽  
Vol 1 ◽  
Author(s):  
Tim Offermans ◽  
Lynn Hendriks ◽  
Geert H. van Kollenburg ◽  
Ewa Szymańska ◽  
Lutgarde M. C. Buydens ◽  
...  
Keyword(s):  
Data Science ◽  
Industrial Process ◽  
Production Quality ◽  
Production Plant ◽  
Monitoring And Control ◽  
Process Data ◽  
Statistical Tool ◽  
Multiple Production ◽  
Path Modelling ◽  
Different Seasons

Understanding how different units of an industrial production plant are operationally related is key to improving production quality and sustainability. Data science has proven indispensable in obtaining such understanding from vast amounts of historical process data. Path modelling is a valuable statistical tool to obtain such information from historical production data. Investigating how relationships within a process are affected by multiple production conditions and their interactions can however provide an even deeper understanding of the plant’s daily operation. We therefore propose conditional path modelling as an approach to obtain such improved understanding, demonstrated for a milk protein powder production plant. For this plant we studied how the relationships between different production units and steps are dependent on factors like production line, different seasons and product quality range. We show how the interaction of such factors can be quantified and interpreted in context of daily plant operation. This analysis revealed an augmented insight into the process that can be readily placed in the context of the plant’s structure and behavior. Such insights can be vital to identify and improve upon shortcomings in current plant-wide monitoring and control routines.

Water Quality Prediction Using Statistical Tool and Machine Learning Algorithm

2020 ◽  
pp. 609-623
Author(s):  
Arun Kumar Beerala ◽  
Gobinath R. ◽  
Shyamala G. ◽  
Siribommala Manvitha
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Statistical Tool ◽  
Data Set ◽  
Water Quality Prediction ◽  
Living Things ◽  
Sampling Locations ◽  
Different Seasons

Water is the most valuable natural resource for all living things and the ecosystem. The quality of groundwater is changed due to change in ecosystem, industrialisation, and urbanisation, etc. In the study, 60 samples were taken and analysed for various physio-chemical parameters. The sampling locations were located using global positioning system (GPS) and were taken for two consecutive years for two different seasons, monsoon (Nov-Dec) and post-monsoon (Jan-Mar). In 2016-2017 and 2017-2018 pH, EC, and TDS were obtained in the field. Hardness and Chloride are determined using titration method. Nitrate and Sulphate were determined using Spectrophotometer. Machine learning techniques were used to train the data set and to predict the unknown values. The dominant elements of groundwater are as follows: Ca2, Mg2 for cation and Cl-, SO42, NO3− for anions. The regression value for the training data set was found to be 0.90596, and for the entire network, it was found to be 0.81729. The best performance was observed as 0.0022605 at epoch 223.

Bayesian networks: Theory, applications and sensitivity issues

2017 ◽  
Vol 01 (01) ◽  
pp. 1630014 ◽  
Author(s):  
Ron S. Kenett
Keyword(s):  
Bayesian Networks ◽  
Expert Opinion ◽  
Data Science ◽  
Information Quality ◽  
Theoretical Background ◽  
Hospital Environment ◽  
Statistical Tool ◽  
Data Set ◽  
Operational Variables ◽  
High Level

This chapter is about an important tool in the data science workbench, Bayesian networks (BNs). Data science is about generating information from a given data set using applications of statistical methods. The quality of the information derived from data analysis is dependent on various dimensions, including the communication of results, the ability to translate results into actionable tasks and the capability to integrate various data sources [R. S. Kenett and G. Shmueli, On information quality, J. R. Stat. Soc. A 177(1), 3 (2014).] This paper demonstrates, with three examples, how the application of BNs provides a high level of information quality. It expands the treatment of BNs as a statistical tool and provides a wider scope of statistical analysis that matches current trends in data science. For more examples on deriving high information quality with BNs see [R. S. Kenett and G. Shmueli, Information Quality: The Potential of Data and Analytics to Generate Knowledge (John Wiley and Sons, 2016), www.wiley.com/go/information_quality.] The three examples used in the chapter are complementary in scope. The first example is based on expert opinion assessments of risks in the operation of health care monitoring systems in a hospital environment. The second example is from the monitoring of an open source community and is a data rich application that combines expert opinion, social network analysis and continuous operational variables. The third example is totally data driven and is based on an extensive customer satisfaction survey of airline customers. The first section is an introduction to BNs, Sec. 2 provides a theoretical background on BN. Examples are provided in Sec. 3. Section 4 discusses sensitivity analysis of BNs, Sec. 5 lists a range of software applications implementing BNs. Section 6 concludes the chapter.

On the topological modeling and analysis of industrial process data using the SOM

2010 ◽  
Vol 34 (12) ◽  
pp. 2022-2032 ◽  
Author(s):  
Francesco Corona ◽  
Michela Mulas ◽  
Roberto Baratti ◽  
Jose A. Romagnoli
Keyword(s):  
Industrial Process ◽  
Process Data ◽  
Modeling And Analysis ◽  
Topological Modeling

Research on the Applications and Characteristics of the Wireless Sensor Network

2014 ◽  
Vol 538 ◽  
pp. 498-501 ◽  
Author(s):  
Ming Zhe Qu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Industrial Process ◽  
Wireless Sensor ◽  
Monitoring And Control ◽  
Spatially Distributed ◽  
Process Monitoring And Control ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Radio Transceiver

A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on. The WSN is built of "nodes" – from a few to several hundreds or even thousands, where each node is connected to one (or sometimes several) sensors. Each such sensor network node has typically several parts: a radio transceiver with an internal antenna or connection to an external antenna, a microcontroller, an electronic circuit for interfacing with the sensors and an energy source, usually a battery or an embedded form of energy harvesting.

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