Paper 360
We surveyed three industry suppliers who work with pulp and paper industry customers to capture and use data in mill environments and asked them for some quick tips on how mills can get the most from their data. Answers were provided by:
• Michael Gee, North American Account Manager – Wedge, Trimble Forestry
• Kai Vikman, COO, Industry Business Group, Pöyry
• Fredrik Westerberg, Global Marketing Director – Pulp, Paper, and Continuous Web Solutions, Honeywell Process Solutions
As a supplier, what are the biggest advancements you’ve seen in what mill data systems can do overall?
Westerberg: The biggest recent advancement in data systems has been in the ability to turn data into meaningful insights through Big Data analysis. This goes far beyond the usual set of reports and graphic displays, which is the typical functionality of mill data systems. The data sources can be from the process, from the assets, or from the people themselves.
With advancements in standards in communication protocols the data can then be integrated into the business systems and used for collaborative decision making across departments within the mill and the enterprise.
Vikman: Every engineering project—big or small—will create different types of data and documentation. This data and documentation has specific storage requirements, including that it has to be available for several different users whenever it is needed. It forms the essential base for the reliable, efficient operation of the manufacturing unit. Asset management over the course of the project’s life cycle is also highly dependent on the accessibility and quality of data and documentation, which must be available in every possible location, including externally for the relevant partners and stakeholders.
To achieve efficient plant operations and maintenance, the plant’s data management system must be continuously updated in an “as built” condition. A structurally well-organized plant data management system enables both external and internal stakeholders to use the system and achieve savings in both time and cost efficiency. With a well-planned plant data management system, the plant can also reduce the storage needed and value of spare parts. With this system, all necessary manuals and machine drawings are available at every stage of the maintenance process—from work order to work planning, execution, and finally to as-built documentation.
Gee: We’ve seen significantly increased production capacity, and a dramatic reduction of rejected products, thanks to advanced data systems. Typically mills have tens of thousands of process measurements and control loops where data are collected. Traditional data systems and methods have only allowed for the analysis of a very small fragment of the available data. Data analysis has been time consuming and laborious. Today’s more advanced tools provide easy analysis of large data sets and propose relevant, expedited answers to complicated questions. As a result, mills are able to find valuable optimization potential hidden in process data, bringing results to everyday operations and decision making.
Decisions that tackle acute problems or mill process adjustments need to be made quickly; valid, accurate answers can now be realized immediately. Plus, efficient data systems can ignore irrelevant data, which in the past would normally destroy analyzing attempts. Innovative data systems can propose trusted answers to questions such as the root cause of unwanted process events and allow for fast fail testing hypothesis in order to eliminate incorrect assumptions.
What is the biggest mistake mills make when implementing a new data system?
Vikman: In any engineering project, the plant data management system and implementation work must start early, so that it is ready when the plant or piece of infrastructure starts operation. For this, all user needs—such as preventive maintenance plans, material master integration to purchasing systems, and material storage work—must be started early enough. The standards need to be in place and clear to every supplier as they provide their technical data to be uploaded in the plant data management system.
Gee: A data system itself does not improve mill performance. The data system must be well designed to integrate easily with each mills’ everyday operations and management goals to achieve best results. This means:
• The same technology platform should be used throughout the organization to support mill goals. This gives management visibility to process and operations; engineering a focus on improvement potential and solving bottlenecks; and operators a user-friendly tool to follow best practices for continuous improvement.
• Data access is often limited to only a sub-set of data, resulting in a low resolution compared to process dynamics; mill data systems must provide real-time, online access to all process data.
• In the past, data systems chosen have not been easy for users to use. The results are poor utilization limited to a few specialists, resulting in increased risk.
Westerberg: As a data systems provider, the biggest mistake we see mills make is not enough effort is spent early on in the project cycle to fully understand the user’s needs. Later in the project this results in excessive customization on the system to suit the needs of the operation. In the end the project comes in way over budget, late, and has tremendous issues to support long term.
The best approach is to keep to the 80/20 rule: about 80 percent should be standard functionality, with at most 20 percent customization, for a particular project.
What’s the most important thing a mill can do to get the most from its data system?
Gee: Mills need to do their due diligence—select a data system with the necessary power, intelligence, and ease of use to deliver results and provide seamless integration with everyday work processes and management practices. This includes:
• Select a proven data system that is easy to use for all levels of the organization, efficient in a mill environment, and demonstrates benefit with real-world process data (outlier values, shutdowns, grade dependency.)
• Integrate seamlessly with everyday operations and management systems.
• Don’t make an assumption before the analysis is performed; i.e., don’t try to find the process data to support a conclusion or assumption. Determine the question(s), use all possible data for analysis and diagnostics, and draw a conclusion based on the data. This requires all data to be available on-line at all times and will set the requirements needed to support the mill data system.
• Data system should be flexible to accommodate new events without configuration.
Westerberg: The system must be designed to be easy for a user to get the information they need and to share the results with others in the organization without having to be an expert in the underlying system technology. Information must be accessible from any location, whether it’s in the mill, at home or at a conference, 24/7, and from any device.
Vikman: To start a plant data management system project, the first thing is to define the standards (technical and commercial), classifications and equipment, and department hierarchy that will be the base for the material master. The standards must be chosen based on the industry regulations and local deviations (ISO, TÜV, ASME, PSK, etc.) Also, the network architecture and data communications rules need to be defined at the beginning.
The system will be as good as the users’ ability to take advantage of the possibilities it creates. The basic needs must be identified before the system is selected and decisions made on how the system can be integrated with other data sources, e.g., ERP-system. The possibilities are huge, starting from basic document archive to real 3D mill with real information about the whole site.
What’s the newest way your company is addressing mill data needs?
Westerberg: To address the need for enterprise wide data history and analysis, Honeywell recently introduced the “Uniformance Connected Historian” cloud-based Big Data enterprise historian. It is offered as a cloud subscription instead of the traditional large capex and resource-intensive IT project, which significantly reduces total cost of ownership for our customers.
Vikman: Pöyry has years of experience of plants’ data management systems, starting from pre- and basic engineering phases and covering the whole lifecycle of the plant. Pöyry Virtual Site design tool information covers all engineering disciplines and can be integrated with all relevant sources and enriched, e.g., with machine supplier 3D models or aerial photogrammetry of site.
Gee: Trimble’s Wedge is an online process diagnostics system that manages Big Data from different sources for expedited analysis and diagnosis of the root cause of a problem. Wedge’s easy user experience gets process operators and engineers up and running instantly without the need for developers and programmers. Wedge delivers an auditable version of the truth and is the predominant solution to routinely tune an organization’s process performance and influence operational efficiencies. WestRock currently uses Wedge as a PI data analysis tool to find data relationships that would not have been visible without it.