Metering and Inputs

The team standardizes naming conventions for unit attributes when collecting and analyzing real-time data. It is also beneficial for data modeling, data exchange, and reporting purposes. They include weather data to look for seasonal patterns that can affect key performance indicators (KPIs) and detailed analysis. Some chemical processes are always affected by current atmospheric pressure, temperature, humidity, and wind speed. Energy consumption is sometimes dependent on raw material types and heating processes.

Data Capture and Reporting

Data must be mapped into the asset data templates from the meters to the programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs).

Peter Argus and Monica Armstrong, the planning and economics coordinator, have created the required analytics to calculate total energy consumption, total water consumption, and other variables in their templates. Their data classification strategy simplifies data aggregation for identifying hidden losses in order to define process improvements that directly reduce refinery costs.

Figure 7.3 shows the refinery block flow and process flow diagrams including auxiliary equipment. The key energy inputs to the refinery are gas, fuel, air, and water required to produce electricity, steam, and compressed air to support the operations. The team also plans to look at gas emissions produced by energy transformation as well as water cooling and treatment.

Ron has been working with Peter and Monica to define the process unit object model, or asset template, for their refinery. Figure 7.4 shows the detailed data model to include all consumable variables in the process unit model. Monica provides the team with the basic model used in their planning and economics optimizer.

Data Analysis, Visualization, and Reporting

Data configuration, data analysis, and reporting efforts are reduced using reusable templates that feature real-time notifications when an exception


Proclndustries simplified energy process diagram.


Unit template for event frame analysis by operating modes.

is detected. Two other ideas added further granularity to the data: (1) capturing process events for data aggregation and (2) using target production values to identify minor operations delays that cause energy and consumable losses.

In addition, the template computation capabilities totalize data; convert values to their desired process engineering units; and normalize the efficiency calculations for conveyors, pumps, compressors, and rotating equipment. The hierarchy and template design simplify information access, providing desired context and detail. The original data at its original resolution is always available. Therefore, the design of the analysis layer determines how successful the applied model or abstraction is.

The EIDI data can be fed to non-operations displays, such as the Microsoft Power BI pivot table shown in Figure 7.5. This interactive consumption report is based on event frames that have captured unit-operating


Excel report depicting real-time aggregation of EIDI production and consumable data by operating mode.

conditions. Total energy consumption is shown, filtered by operational events. The tables are generated by selecting the power table fields shown on the right-hand side of the figure. The table is composed by rows listing all assets, with the right-side column allowing the user to filter consumables and operating modes.

When building this Power BI report, Monica surmised that exporting and including all of the relevant data was key. She decided the user would filter information that the user is not interested in analyzing at a particular time. This approach is simpler and more prudent than not storing the available information, as the information may be needed for additional or future analysis. This also minimizes report maintenance, as we never know what information will be needed in the future for internal reporting, more focused analysis, or extracting information for external regulatory compliance or litigation. (Exporting EIDI data to advanced analytics such as Power BI is extensively covered in Chapter 10.)

A key is to have the event frame template for process units strategically designed for operational intelligence analysis. The event frame template can always be adapted to include additional information if required. The concept is to aggregate the data based on the current variance set by the production schedule. For example, additional consumables can include each type of steam, water, fuel gas, fuel oil, electricity, catalyst, hydrogen, acid, caustic, and other reagents as presented in Figure 7.6.

Figure 7.6 shows the classification of the electrical and water consumption for the five production states selected for analysis. As such, the EIDI has been


Microsoft Excel Power Pivot: Energy and water consumption.

assisting the engineers for all process units to classify the events for aggregation of the data for the five states. The results show that the minor losses caused by times when units are running in trouble mode is almost equal to the running times. These results indicate that there is room for improvement in analyzing the trouble events to find out why the operators have to slow down the units. (The data presented is not real; however, the process and generation of the interactive report is real using the suggested strategy and tools.)

The total energy consumption and the specific energy consumption are tracked in real time. By comparing these metrics with target values, the performance of the unit can be analyzed and opportunities for improvement researched if economically feasible. All of these calculations are performed in the EIDI analytics, and the event-framing strategy provides deeper insights to estimating losses caused by not meeting the targets because of operating troubles, idle time, equipment downtime, or scheduled maintenance.

Once the basic root causes of minor losses are identified and resolved, the team can consider optimizing operations.

Figure 7.7 displays 12 months of refinery energy consumption and feed rates, indicating that during July and August, the refinery was in turnaround mode.


Power BI energy intensity monthly results for one year.

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