* Editor’s note: This article is adapted from a paper presented at the 2015 AIChE Spring Meeting and 11th Global Congress on Process Safety.
The data requirements of a pilot facility are just as important as the plant’s physical components. Follow this guidance to design and deploy a robust process data system for your pilot plant.
The pilot plant stage of process development provides valuable data that must be transformed into actionable information to guide the design and operation of a commercial facility. This stage involves a diverse group of stakeholders, including engineers, scientists, and executives, each with their own unique information needs. A process data system that collects and analyzes data from your pilot plant must meet the requirements of each stakeholder.
This article explains how to design and deploy a process data system for a pilot plant. It identifies the various stakeholders involved in a pilot plant project, the data and information needs of different stakeholders, and the diverse types of data that the system needs to handle. The article lays out a step-by-step process to develop the pilot plant data system and discusses some of the challenges that may be encountered along the way.
A pilot plant data system
The design and implementation of a pilot plant data system is a project unto itself. It may be part of the overall pilot plant project, but it should be treated as a separate project. The disciplines and focus of the personnel who design and deploy these systems are, for the most part, different from those who design and deploy the physical components of a pilot plant.
A dedicated project manager should be assigned to oversee the project. If the company does not have in-house expertise in this field, a qualified and experienced systems integration (SI) firm should be contracted to manage, design, and deploy the system.
Data vs. information vs. knowledge vs. wisdom
A distinction should be made between data and information (Figure 1) (1–2).
Data are raw facts. They have no meaning in and of themselves. They are neither right nor wrong. They just are. Examples of data include:
- the object is red, round, and weighs 4 oz
- the pressure in the vessel is 125 psi.
Information uses data. When data are processed, organized, structured, and/or presented in a given context, they become information. Information can be right or wrong, depending on the context. Examples of information include:
- the object is a fruit. This information eliminates some objects, but does not give enough context to determine exactly what it is. It could be an apple or a tomato; both are fruits. If, however, more context is given, such as the object grows on vines not trees, then it becomes clear that the object is a tomato, not an apple.
- the pressure is increasing but is below the pressure rating of the vessel (150 psi).
Knowledge uses information to come to some conclusion. When multiple pieces of similar information are combined and used as the basis to draw a conclusion, they become knowledge. Knowledge is the understanding of patterns of information. Examples of knowledge include:
- even though a tomato (the object) is a fruit, it is rarely used in fruit salad
- if the pressure continues to increase above the vessel’s rating (150 psi), the vessel will burst.
Wisdom uses knowledge to correct, alter, or prevent a situation. Wisdom enables you to take action based on knowledge. For example:
- I won’t put tomatoes in my fruit salad
- I will open the pressure relief valve on the vessel to relieve the pressure before it bursts.
Actionable information. The term “actionable information” is used throughout this article to refer to data that have been transformed into information from which knowledge can be gleaned and then judged to take a wise action.
Diverse stakeholders and data sources
When developing a pilot plant data system, it is important to keep in mind both the end (data out) and the beginning (data in). At the end are multiple stakeholders with diverse information needs; at the beginning are data of many different forms.
Multiple stakeholder sets. In many situations, sometimes the best place to start is at the end. This is true of data transformation in a pilot plant. It must be understood that several different types of people will need access to the data from your system. These groups of people are referred to as stakeholder sets. A typical pilot plant data system may have many stakeholders:
- maintenance personnel
- quality assurance personnel
- plant executives
- corporate executives.
Your particular pilot plant may include some or all of these stakeholder sets and maybe others. The important thing to remember is that each set will have its own requirements concerning how the data stored in the system are transformed into actionable information.
Divergent data sources. With the end in sight, now let’s focus on the beginning. Most pilot plants have more than one source of data. A typical pilot plant involves four divergent sources of data (Figure 2).
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