Meeting decarbonisation targets

With energy management being central today for good operational performance and critical for future decarbonisation, such domains need to have the real-time ability to quickly investigate process deviations. They can directly investigate either from processing upsets or critical equipment reliability issues impacting unplanned outages. In both cases, process experts can build work flows around specific process analytics solutions which complement the classical automation layers. This approach will provide more compliant operations and higher reliability, leading to greater plant-wide availability.

A self-service analytics solution that uses pattern recognition can be utilised by process experts, without the need for data scientists, to investigate a wide range of factors affecting process priorities such as safety, legislation compliance, plant reliability, margin and cost. They can use this solution’s search engine and root cause analysis features to identify both similar and deviating process patterns which can offer early warning and elimination of sub-optimal operations and unplanned downtime. A self-service analytics tool will allow process experts to achieve operational excellence by easily managing energy, finding the best energy operating point for utilities, reducing process energy consumption, improving transient operations to startups and process events, and monitoring cross-assets. Clearly, there has never been a more critical time for companies to embrace the potential and value of digital transformation and advance analytics technology.

Optimising energy management

Complex refineries involve an assembly of interconnecting smaller yet significant plants, each with its own thermal energy consumption. Significant effort is involved in reconciling physical material transfer across the integrated network of process units. Explanation of daily and monthly deviations from desired values is often the task of domain expert process unit engineers. A refinery data historian typically captures data from thousands of points in time, many constraining in value. For example, such points include hundreds of direct plant and quality instrument measurements, key calculation variables, controller/valve/status outputs, laboratory measurements in each process area, and scheduled feed information.

Figure 1. Data visualisation and cleaning in order to optimise the energy consumption of a distillation column.

From a thermal energy perspective, process engineers battle to understand the many significant operational variations typically occurring over a monthly reporting period. With a mixture of slow and fast dynamics, these variations may involve slow process performance deterioration caused by feedstock related critical equipment fouling, faster daily feed compositional variations, product grade/mode changes, and even faster but constraining fractionation column pressure control, and many more factors. Together with process upset events and occasional equipment failures, determining the best possible specific-energy recovery per feed or operational mode type becomes a time-consuming and difficult challenge.

The heart of this challenge is to determine the best operating profiles from past data that contain the ideal energy recovery landscape. This can only be achieved if an understanding of the causes of such variations are first defined and then eliminated. With a self-service analytics solution, a process engineer has the ability to optimise energy consumption as part of any energy management system. A self-service analytics solution can be used as a real-time exception-based surveillance tool that removes the need to consider simulation methods for investigating difficult areas such as non-linear trade-offs between energy and feedstock throughput. If the optimal energy consumption conditions are in the historic data, this analytics tool can very easily and quickly detect these periods using pattern recognition and eliminate correlated root causes via its recommendation engine.

Finding the best energy operating point for utilities

The generation and management of common energy supplies is central to most refining operations. For example, large boilers produce steam at different pressure levels for distributed service around the refinery process units. Balancing multiple boiler fuels with wider refinery requirements while at the same time responding to changing process variation demands remain the key roles of process control and automation systems. A self-service analytics solution would be greatly beneficial in identifying the best proven energy operating point for process situations, such as the typical day-to-day processing plant variations in energy demand from crude feed changes, mode changes, or general process upsets.

A self-service analytics solution has the capability to perform data preparation and filtering and searches through its value-based or pattern recognition functionalities. It can also perform root cause analysis and real-time monitoring to capture the many casual variations from across refinery-wide operations and build a predictive analytics alert solution – all of which aid in determining the best proven energy operating point. Such real-time solutions offer early warning of deviations and elimination of sub-optimal operations and reduced risk of critical equipment unplanned downtime caused by unknown operations outside of operating windows. With high throughputs, small percentage changes in operational availability can result in millions of dollars of bottom line benefit every year.

Reducing process energy consumption

An example of how a self-service analytics solution can be used to reduce process energy consumption is when unnecessarily high column pressure levels occur at different times. These can be due to many variables that require management, such as those involving column feed phase and compositional changes. Monitoring and real-time operational management, among others, is also needed for column heat input affecting column vapour/liquid traffic and diurnal ambient air temperature variations which impact the overhead condensing capacity. High fidelity offline first principle simulation models can support the explanation of any pressure deviation, but these are expensive and time-consuming given the repeated need for calibration and model maintenance. However, this is where a self-service analytics solution can come into play, by utilising the wealth of data from historians. With a tool that uses advanced pattern recognition technology, best operating windows can be established and then used to set automated real-time alert notifications for column pressure excursions, giving process experts time to take data-driven decisions and actions.

Other key performance indicators affecting energy consumption can be established in the same way. Examples include early warning and prevention of unexpected variations in minimum over-flash, stripping steam rates, Claus treating unit H₂S/SO₂, and catalytic cracker regenerator CO/CO₂ ratio values, to name a few.

Together with hierarchical exception-based reporting dashboards, compliant energy consumption can be monitored in real-time with all deviation causes identified and explained. More generally, a self-service analytics solution can offer an operational real-time basis for establishing energy site-wide best practices.

Improving transient operations: start-up events and process upsets

For refineries, it is highly desirable to achieve more timely steady state plant operating conditions using historic best practices for process events. These include events such as major plant turnarounds, start-ups following trips, and major process disturbances.

For plant wide or specific equipment trips, product grade mode or feedstock compositional changes, a self-service analytics solution can provide experts with the ability to guide operations through the most effective transitional mode change(s) in real-time. Using this tool, they can create highly visual desirable operating windows for such events from similar historical events. They can then track progress via their own created fingerprints of golden start-up profiles and activate the fingerprints automatically to monitor future deviations, should they occur. This approach can be adopted either as part of a wider operations standard workflow for managing start-ups or a production mode change. Such a tool is a powerful means to operate with confidence during transient events.

Monitoring cross-assets

When there are several assets or units of operations with high similarities, process experts can greatly benefit from a systematic and thus efficient approach to perform analysis. Some examples include plant inlet pumps, boiler units to produce steam, and identical trains processing acid gas to remove sulfur. For these cases, analysis performed on one asset could be applicable to its similar pairs. Other refinery examples are compressors and compressor stages present at processes such as hydrogen recycle, fluid catalytic cracker unit (FCCU) main air blower, and alkylation refrigeration. Most of these compression operations involve two or more stages, and with TrendMiner, it is possible to easily check if a variation of, for instance, the feed pressure and power of one stage is similar to the others. This capability is particularly valuable in performing root cause analysis, benchmarking exercises, and cross-asset monitoring.

Figure 2. Cross-asset monitoring for sales gas compressors.

Conclusion

The potential and value of digital transformation in conjunction with advanced analytics technology are substantial. A self-service analytics solution can be used to make energy management easier, providing real-time surveillance of operations. It can also be used to find the best energy operating point for utilities as it helps process experts understand operational variations. Additionally, it can be used to generate key performance indicators which affect energy consumption, and to improve transient operations. Advanced self-service analytics is becoming a fundamental factor in maximising downstream business reliability, profitability, and sustainability, so these organisations can realise their climate strategies and meet their decarbonisation targets while at the same time achieve operational excellence. For those businesses that are lagging behind in their digitalisation transformations and adoption of advanced analytics technology, the time is now to implement strategies to adopt them.

Written by Julian Pereira, TrendMiner, Belgium.

Read the article online at: https://www.hydrocarbonengineering.com/special-reports/01122020/meeting-decarbonisation-targets/