Comparison of signal processing methods considering their optimal parameters using synthetic signals in a heat exchanger network simulation

Thibault, Émilie; Désilets, Francis Lebreux; Poulin, Bruno; Chioua, Moncef; Stuart, Paul

doi:10.1016/j.compchemeng.2023.108380

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…To achieve that, the wavelet transform are used. Some approaches reported in the literature are compared in [72]. It is highlighted that low-pass filters do not have the ability to reduce noise present at different frequencies and that lead to the development of multi-level analysis.…”

Section: Step 2: Signal Processingmentioning

confidence: 99%

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

Thibault

Kelly²,

Désilets

et al. 2023

Processes

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Data management systems are increasingly used in industrial processes. However, data collected as part of industrial process operations, such as sensor or measurement instruments data, contain various sources of errors that can hamper process analysis and decision making. The authors propose an operating-regime-based data processing framework for industrial process decision making. The framework was designed to increase the quality and take advantage of available process data use to make informed offline strategic business operation decisions, i.e., environmental, cost and energy analysis, optimization, fault detection, debottlenecking, etc. The approach was synthesized from best practices derived from the available framework and improved upon its predecessor by putting forward the combination of process expertise and data-driven approaches. This systematic and structured approach includes the following stages: (1) scope of the analysis, (2) signal processing, (3) steady-state operating periods detection, (4) data reconciliation and (5) operating regime detection and identification. The proposed framework is applied to the brownstock washing department of a dissolving pulp mill. Over a 5-month period, the process was found to be in steady-state 32% of the time. Twenty (20) distinct operating regimes were identified. Further processing with the help of data reconciliation techniques, principal component analysis and k-means clustering showed that the main drivers explaining the operating regimes are the pulp level in tanks, its density, and the shower wash water flow rate. Additionally, it was concluded that the top four persistently problematic sensors across the steady-state spans that would need to be verified are three flow meters (06FIC137, 06FIC152, and 06FIC433), and one consistency sensor (06NIC423). This information was relayed to process experts contacts at the plant for further investigation.

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Section: Step 2: Signal Processingmentioning

confidence: 99%

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

Thibault

Kelly²,

Désilets

et al. 2023

Processes

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“…This noise has the potential to introduce fluctuations in the feedback signals utilized by the controller, consequently resulting in suboptimal or erratic control performance [9]. To mitigate the effects of measurement noise in monitoring systems, several techniques have been used, such as filtering algorithms [10], signal processing methods [11], and software sensors [12]. Software sensors, commonly referred to as observers (or virtual sensors), have emerged as a viable substitute for addressing the issue of measurement noise and the unavailability of hardware sensors due to system configuration or cost.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fractional Multi-Order High-Gain Observer Design to Estimate Temperature in a Heat Exchange Process

Borja-Jaimes,

Adam-Medina,

García-Morales

et al. 2023

Axioms

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In the present manuscript, we design a fractional multi-order high-gain observer to estimate temperature in a double pipe heat exchange process. For comparison purposes and since we want to prove that when using our novel technique, the estimation is more robust than the classical approach, we design a non-fractional high-gain observer, and then we compare the performance of both observers. We consider three scenarios: The first one considers the estimation of the system states by measuring only one output with no noise added on it and under ideal conditions. Second, we add noise to the measured output and then reconstruct the system states, and, third, in addition to the noise, we increase the gain parameter in both observers (non-fractional and fractional) due to the fact that we want to prove that the robustness changes in this parameter. The results showed that, using our approach, the estimated states can be recovered under noise circumstances in the measured output and under parameter change in the observer, contrary to using classical (non-fractional) observers where the states cannot be recovered. In all our tests, we used the normalized root-mean-square, integral square error, and integral absolute error indices, resulting in a better performance for our approach than that obtained using the classical approach. We concluded that our fractional multi-order high-gain observer is more robust to input noise than the classical high-gain observer.

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“…This framework considers, among the other steps, the reduction in measurement and process noise in process data. Different data processing techniques to reduce those high-frequency components, i.e., noise, are compared in [8]. The wavelet transforms were found to be robust and were used to establish heuristics to process a wide range of signals in a timely, efficient manner.…”

Section: Introductionmentioning

confidence: 99%

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision-Making—Part 2: Framework Application Considering Activity-Based Costing Concepts

Thibault,

Ledoux,

Stuart

2023

Processes

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Operating time series data collected and stored in historian must be managed to extract their full potential. Part 1 of this paper proposed a structured way (a sophisticated approach) to process industrial data; this first part explains in detail the data processing framework used as the basis for the costing analysis present in the second part of this series. The framework considers the analysis scope definition, data management steps, and operating regimes detection and identification. The added value of this proposed framework is demonstrated in Part 2 via the use of cost accounting for operational problem-solving (debottlenecking), i.e., its practicality is validated via its application alongside a cost analysis on the brownstock washing department of a kraft pulp mill. The traditional debottlenecking approach assumes a single operating condition considering that operating regimes allow for a much more sophisticated debottlenecking study of the washing department. With the use of operations-driven cost modeling (contingent on activity-based costing concepts) and processed time series data corresponding to steady-state operation, incremental profit can be assigned to each operating regime in order to identify the most cost-efficient one. The overall objective of this two-part series is to convert processed industrial steady-state data and cost information into knowledge that can be used to optimize the washing department of a chemical pulp mill. More specifically, different operating regimes are assessed, and the most suitable operating strategy is defined. The application of activity-based costing on a large amount of historically processed industrial data led to the improvement in the operation. The identified optimal way to operate (pulp throughput, pulp conductivity, defoamer and bleaching chemical quantity, etc.) led to a profit of CAD 49 M per year. Lastly, a contribution analysis of the regimes based on PCA highlighted how the process was operated when the preferred performances happened.

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Comparison of signal processing methods considering their optimal parameters using synthetic signals in a heat exchanger network simulation

Cited by 5 publications

References 47 publications

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

A Novel Fractional Multi-Order High-Gain Observer Design to Estimate Temperature in a Heat Exchange Process

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision-Making—Part 2: Framework Application Considering Activity-Based Costing Concepts

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