Conventional data systems are designed for automated analysis, predefined workflows and dashboards for routine monitoring and reporting. They are good, but not sufficient for real-life troubleshooting, which is complex and nonlinear in nature.

When a process engineer must tackle new phenomena or unexpected issues, ad hoc data analysis is required. This exploratory work rarely follows a strict, system-driven sequence; rather, it proceeds in undefined steps of investigation, requiring analysts to adapt their approach as insights continually evolve.

For this essential, creative problem-solving to flourish, the data analysis platform must empower individual users to easily and intuitively create and fine-tune their environment for specific tasks. 

Customization: The key to agile troubleshooting and process optimization 

A highly effective data analysis tool must serve the user’s “mindflow,” not compel them to adhere to a rigid theoretical workflow. This flexibility is realized through deep personalization capabilities:

1. On-the-Fly Customization: The capability to define one’s own workspaces, customizing the layout and selecting visualization options via intuitive drag-and-drop functionality, is essential. This allows process experts to tailor dashboards and create ad-hoc diagrams in seconds to contextualize a specific issue.
2. Intuitive Data Refinement: Raw data rarely offers reliable analysis results immediately. Domain expertise is needed to judge which values are irrelevant, necessitating tools that support immediate data cleansing, filtering, and refinement. This includes the crucial ability to compensate for complex process delays (lags) to ensure reliable correlation findings.
3. Ad Hoc Calculation Engine: To derive truly useful measurements, users frequently need to combine information from several sources, performing unit conversions or creating new calculated measurements (soft sensors). Systems must include an embedded calculation engine that allows users to easily create and utilize these custom measurements in diagrams and analyses as if they were real physical measurements.
4. Accessible Causal Analysis: Understanding the “why” is the ultimate goal. The system must empower users to intuitively apply advanced analysis methods to uncover hidden root cause-and-consequence relationships. This functionality must be intuitive, enabling process experts to diagnose the source of anomalies without requiring data science intervention.

The best data analytics tools combine automation and customization capabilities

For effective data analysis and utilization, the best data analysis systems enable both automated analysis and flexible customization.

Standard views and automated reports fulfill the need for consistency and knowledge dissemination, but they are insufficient for the agile, spontaneous demands of modern troubleshooting and process optimization. 

The competitive edge belongs to organizations that deploy systems that allow users to seamlessly modify datasets, views, and analytic parameters, ensuring that the system is always precisely tuned to the singular task—no matter how novel—that the user is trying to solve.

 

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In the fiercely competitive paper industry, consistent, high-quality paper isn’t just a goal—it’s the bedrock of profitability and customer satisfaction. However, traditional methods of monitoring paper quality often fall short, leaving valuable insights buried and costing time and money. This article explores how integrating Cross Direction (CD) profile measurements with process data can overcome these challenges, offering a smarter approach to paper production.

Why Traditional Methods Fall Short

For decades, paper quality has been monitored using visual 2D-profile maps. While effective for identifying major flaws, these maps struggle to reveal deeper, detailed information crucial for understanding quality deviations. The inability to connect these visual maps to process measurements creates a critical blind spot, making it difficult to pinpoint the root cause of paper quality disruptions. To truly master quality, robust methods are needed to manage extensive historical data and understand the impact of process variable changes on profile stability.

Decoding Profile Deviations

No two paper profiles are perfectly identical. While an ideal scan would show perfectly uniform values, real-world production always introduces some variation. The goal isn’t zero variation but rather values that are normally distributed around a set point with minimal spread. Spotting deviations from this optimal state quickly is paramount. 

Profile changes primarily fall into two categories:

• Changes in the shape of scan vectors: These range from simple slopes to complex U-shapes, S-shapes, or W-shapes.
• Changes in the statistical distribution of scan values: Analyze characteristics like mean value, deviation, distribution symmetry (skewness), and overall distribution shape (kurtosis).

Combining insights from profile maps with process data dynamics generates specific profile variation indices—metrics that signal the emergence of profile anomalies. Tracking these indices as trends allows for direct analysis alongside other process measurements, improving the ability to identify the causes of profile changes. This approach also enables efficient study of long-term trends in paper profile uniformity.

Introducing the Wedge Advantage

Trimble’s Wedge system offers a transformative solution by treating intricate profile data with the same intelligence and flexibility as any other process measurement. This advanced data analytics system empowers users to:

• Dive Deep: Freely select any time frame for study, whether it’s a single production run or a specific paper grade over months. Filter out outlier data points to ensure clear analysis.
• Characterize Deviations: Calculate distinct indices that precisely characterize and pinpoint problems within profile measurements, turning visual anomalies into actionable insights.
• Uncover Root Causes: Compare profile variation indices with process measurements to quickly identify process changes triggering shifts in CD profiles, ensuring consistently uniform and high-quality paper.

From Monitoring to Mastery

The Wedge system employs advanced pattern recognition to diagnose complex profile shapes, such as a W-shaped profile. It analyzes relevant process measurements to identify correlations that occurred concurrently with or before the profile deviation, generating a prioritized list of potential root cause candidates. The pattern-recognition tool compensates for process delays, ensuring accurate root cause identification.

This integrated methodology significantly enhances the ability to swiftly identify the root cause of paper profile problems, ensuring consistent production of uniform, high-quality CD profiles. Defined by reliability, speed, and user-friendliness, the Trimble Wedge system streamlines the diagnostic process, elevating paper quality and contributing to operational efficiency and sustained profitability.

Learn more 

If you are interested in delving deeper into the innovative methodologies discussed in this article, Trimble offers a detailed white paper titled “Improve Paper Quality by Combining Cross Direction (CD) Profile Measurements with Process Measurements.” This document provides an in-depth exploration of the techniques and benefits of integrating profile and process data. To request your copy and gain further insights into optimizing paper production, please contact wedgesales@trimble.com.

The pulp and paper industry is constantly evolving, driven by the need to optimize processes and uphold high-quality standards. As traditional methods of data monitoring struggle to keep pace with these demands, innovative data analytics approaches are emerging as vital tools. This article examines the roles of ad hoc and automated data analysis, highlighting their contributions to enhancing operational efficiency and decision-making in this complex sector.

Ad Hoc Data Analysis: Flexibility and Exploration

Ad hoc data analysis is characterized by its flexibility and exploratory nature. It is particularly valuable for addressing new and often unanticipated questions, troubleshooting, and exploring new phenomena. This approach involves a dynamic study of data, allowing analysts to adapt their methods based on evolving insights and the problem at hand.

Ad hoc analysis is iterative and non-linear, often requiring multiple cycles of redefining tasks, selecting measurements, refining and analyzing data, and drawing conclusions. This flexibility makes it ideal for generating fresh insights and understanding process behavior dynamically. For instance, when a mill experiences a sudden drop in paper quality, ad hoc analysis can be employed to investigate various factors such as raw material quality, machine settings, and environmental conditions. By iteratively refining their analysis, analysts can identify specific issues and take corrective actions.

However, the iterative nature of ad hoc analysis can be time-consuming and requires skilled analysts and significant resources. Additionally, the lack of predefined workflows can lead to variability in the analysis process, potentially affecting the repeatability of the results.

Automated Data Analysis: Consistency and Efficiency

Unlike ad hoc methods, automated data analysis employs a systematic and structured approach that capitalizes on existing process knowledge and is characterized by its repeatability. 

This technique emphasizes data visualization, pre-configured workflows and process modeling. It incorporates predefined algorithms, dashboards, and reporting tools to process data. For instance, automated dashboards can track key performance indicators (KPIs) such as production rates, energy consumption, and quality metrics, offering real-time insights and alerts for swift decision-making.

The advantages of automated analysis include its consistency, efficiency, scalability, and the capacity to spread established information across the organization. However, it is less adaptable to new or unforeseen situations and relies heavily on existing understanding. Incorrect assumptions or models can lead to inaccurate outcomes, and substantial initial investment and ongoing maintenance are necessary to maintain accuracy and relevance.

Integrating Ad Hoc and Automated Approaches

Both ad hoc and automated data analysis play crucial roles in the pulp and paper industry. Each approach has unique contributions to process optimization and decision-making, and their integration can lead to more robust and adaptive operations.

By integrating both approaches, organizations can achieve a balance between flexibility and consistency, leading to enhanced operational efficiency and improved quality control. Ad hoc analysis provides the flexibility to respond to new challenges and opportunities, while automated analysis ensures the widespread application of proven insights. Insights gained from ad hoc analysis can be incorporated into automated workflows, continuously improving the accuracy and relevance of automated systems.

Requirements for Data Analysis Tools

To effectively support both ad hoc and automated data analysis, tools must possess distinct capabilities. For ad hoc analysis, tools should allow dynamic data exploration, support advanced analytical techniques, and facilitate an iterative workflow. They should also integrate seamlessly with various data sources and offer a user-friendly interface to enable quick adaptation to new challenges.

For automated analysis, tools should support predefined workflows and models, enable real-time data monitoring, incorporate statistical process control (SPC), and be scalable to handle large volumes of data. They should also facilitate the dissemination of established knowledge across the organization.

Conclusion

In conclusion, integrating ad hoc and automated data analysis can significantly enhance the pulp and paper industry’s ability to optimize processes and maintain high-quality standards. By leveraging the strengths of both methods, organizations can achieve a balanced approach that is both adaptive and consistent, ultimately leading to more robust and efficient operations.

Learn more 

For those interested in exploring the innovative methodologies discussed in this article further, Trimble provides a comprehensive white paper titled “Innovative Data Analytics in the Pulp and Paper Industry: Balancing Ad Hoc Insights with Automated Consistency.” This document offers an in-depth examination of the techniques and advantages of integrating ad hoc and automated data analysis. To request a copy and gain additional insights into optimizing processes in the pulp and paper industry, please contact wedgesales@trimble.com.