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

Offermans, Tim; Hendriks, Lynn; Kollenburg, Geert H. van; Szymańska, Ewa; Buydens, L.M.C.; Jansen, Jeroen J.

doi:10.3389/frans.2021.721657

Cited by 4 publications

(1 citation statement)

References 23 publications

(20 reference statements)

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“…Path analysis or the structural equation modeling (SEM) approach is useful for process interpretation because it estimates the structural relationships between process variables. PLS-SEM (Hair et al (2011)) and process PLS (van Kollenburg et al (2021)) have been applied to structural modeling of industrial production processes (van Kollenburg et al (2020); Offermans et al (2021)). However, the relationship of process variables must be manually determined before constructing a model with these structural methods.…”

Section: Introductionmentioning

confidence: 99%

Causal analysis of nitrogen oxides emissions process in coal-fired power plant with LiNGAM

Saito

Fujiwara²

2023

Front. Anal. Sci.

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Coal has been an important energy source worldwide; however, it is the largest source of nitrogen oxide (NOx) emissions because the amount of nitrogen in coal is larger than that of other fossil fuels. Precise control of NOx emissions is required in operations of coal-fired power plants from the viewpoint of air pollution control. Although theoretical analyses of NOx generation from a coal-fired power plant have been conducted, it is difficult to precisely predict NOx generation in an actual plant. NOx generation is affected by various factors, such as furnace design and operating conditions, and there are complicated relationships among them. Thus, it is necessary to identify important operating factors that affect NOx generation in actual coal-fired power plants. A linear non-Gaussian acyclic model (LiNGAM) is an exploratory causal analysis method that identifies a causal ordering of variables and their connection strengths without any prior knowledge of causal relationships among variables. In this study, we analyzed real operation data collected from a coal-fired power plant using LiNGAM to identify factors of NOx generation. The causal relationship between process variables and NOx generation was estimated by means of LiNGAM, and the connectional strengths of the variables on NOx generation were derived. The analysis results agreed with previous reports on NOx generation mechanisms, such as combustion air temperature, steam temperature on a specific side of the furnace, and air flow rate of forced draft fans. In addition, we found the steam flow rate and the furnace pressure as new candidate factors of NOx generation through causal analysis using LiNGAM, which heretofore has not been suggested. Our analysis result should contribute to reducing NOx emissions from coal-fired power plants in the future.

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Section: Introductionmentioning

confidence: 99%

Causal analysis of nitrogen oxides emissions process in coal-fired power plant with LiNGAM

Saito

Fujiwara²

2023

Front. Anal. Sci.

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Near real-time predictions of renewable electricity production at substation level via domain adaptation zero-shot learning in sequence

Teng,

Cambier van Nooten,

van Doorn

et al. 2023

Renewable and Sustainable Energy Reviews

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Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements

Cairoli¹,

Doel²,

Berber³

et al. 2023

npj Clean Water

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To safeguard the quality of river water, a comprehensive approach is required within the European Water Framework Directive. It is vital to conduct non-target screening of the complete chemical fingerprint of the aquatic ecosystem, as this will help to identify chemicals of emerging concern and uncover their unusual dynamic patterns in river water. Achieving this goal calls for an advanced combination of two measurement paradigms: tracing the potential pollution path through the river network and detecting the numerous compounds that constitute the chemical composition, both known and unknown. To address this challenge, we propose an integrated approach that combines the preprocessing of ongoing Gas Chromatography Mass Spectrometry (GC-MS) measurements at nine sites along the Rhine using PARAllel FActor Analysis2 (PARAFAC2) for non-target screening, with spatiotemporal modelling of these sites within the river network using a statistical path modelling algorithm called Process Partial Least Squares (Process PLS). With an average explained variance of 97.0%, PARAFAC2 extracted mass spectra, elution, and concentration profiles of known and unknown chemicals. On average, 76.8% of the chemical variability captured by the PARAFAC2 concentration profiles was extracted by Process PLS. The integrated approach enabled us to track chemicals through the Rhine catchment, and tentatively identify known and as-yet unknown potential pollutants, including methyl tert-butyl ether and 1,3-cyclopentadiene, based on non-target screening and spatiotemporal behaviour.

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

Cited by 4 publications

References 23 publications

Causal analysis of nitrogen oxides emissions process in coal-fired power plant with LiNGAM

Causal analysis of nitrogen oxides emissions process in coal-fired power plant with LiNGAM

Near real-time predictions of renewable electricity production at substation level via domain adaptation zero-shot learning in sequence

Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements

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