The story behind the data |

The story behind the data

The story behind the data

By Robert Golightly, AspenTech

Making sense of vast volumes of production data is the essence of asset optimization. If you cannot see what you do not know, then you will not be able to make the right decisions to optimize production and be competitive. The inside story of operational intelligence lies in rich visualization and analytics capabilities that improve production execution by enabling process manufacturers to quickly identify and resolve operational issues.

The challenge for many refineries and chemicals companies in today’s market is to improve the analysis process and incorporate product characteristics and other non-time series data. It is those data elements that provide additional context for an improved understanding of conditions that limit production operations.

Cutting-edge manufacturing execution systems (MES) integrate the operation, using real-time business performance management to optimally plan, execute, monitor and respond to change immediately across all time horizons. Information must be relevant, timely and collaborative. With discoverable data displayed in a clear, easy-to-understand trends, process manufacturers can efficiently resolve operational issues to remain profitable.

Identifying the problem

Batch analysis often lacks the ability to visualize data in flexible formats or incorporate characteristic data quickly to identify and correct the root causes of operational problems. To gain a thorough understanding of the asset and its performance requires powerful data visualization technology, which can put plant information into context, so that operations personnel can apply timely corrective actions to address important operational issues, like bottlenecks, yield efficiency, variability in product quality and overall asset effectiveness.

Visualization techniques are mainly appropriate for the three types of variability in batch processing: average over time, within-batch and batch-to-batch. Event data is difficult and time-consuming to incorporate in data visualization. Significant time and effort is therefore, required to find, extract and include non-time series data like product characteristics.

Modern manufacturing is like an ecosystem of interconnected software and hardware that helps chemical and petrochemical companies optimize plants and achieve operational excellence. As businesses generate vast amounts of data, efficient decision support solutions are needed to make sense of vital information and ensure operations can adapt quickly to dynamic conditions.

Understanding the data

Automated MES decision support allows companies to make corrective decisions faster to achieve operational efficiencies and deliver greater productivity. Bridging the gap between the technical operation of the plant and the commercial transactions using the latest digital environment (i.e. combination of web, mobile, tablet) provides users with vital information at the right time, allowing staff to dynamically keep up-to-date with operational challenges, anytime and anywhere.

There are currently web-based solutions available that allow users to convert production and business data into operational intelligence with the ability to visualize, analyse, monitor and access data in a clear, graphical display all in one single platform. The visual graphics help engineering users to understand the patterns in the data and associate them with conditions in the plant. This means that users can quickly identify issues and correct problems in production. The data discovery tools bring the most actionable insights to the surface.

The most effective applications build, data discovery into a tool with HTML5 graphics and fast user interfaces, which addresses the common challenge of trying to find quickly the most relevant production information. Users can search multiple sources of data, identify and analyse the key issues and relate it to the actual physical asset of the plant.  

These advanced solutions can present years’ worth of data in an instant, along with annotations and other unstructured information such as alarms, events allocation and other items that provide context, which enables the user to swiftly understand what is happening in the plant. Events are really flexible and users can plot periods of time where operators have placed comment markers or where alarms exist. The rich discovery tools work seamlessly, whether the process is batch or continuous, and event analysis overlays multi-events and trains, such as x - y frequency and histograms, and can be applied with a batch, batch-to-batch or over time. Many of these capabilities can also be used in continuous processes (e.g. evaluating plant performance across different crude types).

Performance analytics include overall equipment effectiveness (OEE). This allows users to compare the performance of one piece of equipment with another and then take the learning from the better performing equipment and apply them to those underperforming areas to help increase the overall plant performance. As a web-based product, these tools do not need to be installed on individual desktops belonging to thousands of users. The tool is intuitive, easy-to-use and can be customised to allow users to only see what they need.

These enhanced capabilities improve analysis by capturing context. Users can easily visualize all pertinent information regardless of the type. Instead of simply looking for anomalous trends, contextual data can be viewed alongside process data to show what is happening in production, thereby delivering greater insights into the source of problems.

Context is crucial

Harnessing data analytics to support faster and better decisions will drive production to deliver products to the highest standards. With advanced MES software, analysis is easier, faster and more effective, allowing process engineers to build a complete picture of production performance from continuous to batch. The rich context and analytics tools help drive greater collaboration across the enterprise by enabling benchmarking and sharing of best practices for faster decision-making. Those companies that adopt best practice and use the power of data visualization software tools can understand the story behind the data and ensure that production outcomes meet with a profitable ending.

Robert Golightly

Robert leads aspenONE Manufacturing Execution Systems Product Marketing. He is an eight-year AspenTech veteran who previously headed up the marketing effort for the company’s APC solutions set. Prior to AspenTech, Robert was Director of Product Management and Marketing at Pavilion Technologies. He has an in-depth background in the process industries and in factory automation for the semiconductor industry, where he led a team that developed advanced application prototypes in applied statistics for manufacturing.