Industrial facilities collect large volumes of data, store them according to prescribed protocols, and then interpret them for process decision‐making. Several sources and types of error contaminate these data for various reasons, but especially because they come from unreliable or unpredictable instruments. Data (or signal) processing corrects measurement errors to improve fidelity. Here, we highlight decision‐making applications and signal processing methods. To fully appreciate the state‐of‐the‐art, we interviewed plant data experts and software developers in the pulp and paper industry to examine how they apply signal processing methods in the context of decision‐making, including the value of process data, how these data are used, and the major barriers that prevent plants from using data. Process experts clean data thoroughly with basic approaches compared to the advanced techniques available in the recent literature. The interviews demonstrate that decisions in industry are primarily based on steady‐state process operating data. Challenges and barriers that prevent the use of process data to their full potential relate to resource limitations (people, time, and money), an entrenched culture, and access to recent technology. In practice, experts consider, implicitly or explicitly, data that represent the process operating under steady‐state conditions. A plant model that represents steady‐state operations is easier to interpret, is presented in a form that is usable by plant operators, and in this way, better enables decision‐making.

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.

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.

Operating data collected and stored in historian must be managed to extract their full potential. Part I of this paper proposed a structured way to process industrial data. This framework considers the analysis scope definition, data management steps and operating regimes detection and identification. The value of this proposed framework is demonstrated in Part II through the use of cost accounting for operational problem-solving. With the use of operations-driven cost modeling (contingent on activity-based costing concepts) and processed data corresponding to steady-state operation, incremental cost reduction can be assigned to each operating regime in order to identify the most cost-efficient one. The overall objective of this paper 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.

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. Therefore, in order to take full advantage of collected and stored data and to increase data quality, an operating regime-based data-processing framework for industrial process decision-making is proposed in this paper. This systematic and structured approach includes the following stages: (1) scope of the analysis, (2) data management and (3) operating regime detection and identification. All steps are based on the combination of process knowledge and data-driven approaches. The proposed framework is applied to data from a brownstock washing department of a dissolving pulp mill, and employed in a case study presented in Part II of this publication, where, considering an activity-based costing analysis, the optimal way to operate the department is identified.