Our White Papers

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Evolving Best-Practices Through Simulation-Based Training: Training The Field Operator Of The Future

Simulators are widely recognized as essential to process control training as they facilitate the propagation of a company’s standard operating procedures (SOPs). This paper explores the use of process control simulators by Chevron Products Company to challenge existing corporate SOPs and to help achieve improvements in overall production performance.

Reducing Energy Cost Through Improved Disturbance Rejection

Two of the most popular architectures for improving regulatory performance and increasing profitability are 1) cascade control and 2) feed forward with feedback trim. Both architectures trade off additional complexity in the form of instrumentation and engineering time for a controller better able to reject the impact of disturbances on the measured process variable. These architectures neither benefit nor detract from set point tracking performance. This paper compares and contrasts the two architectures and links the benefits of improved disturbance rejection with reducing energy costs in addition to improved product quality and reduced equipment wear. A comparative example is presented using data from a jacketed reactor process.

Model-Based Tuning Methods For PID Controllers

The manner in which a measured process variable responds over time to changes in the controller output signal is fundamental to the design and tuning of a PID controller. The best way to learn about the dynamic behavior of a process is to perform experiments, commonly referred to as “bump tests.” Critical to success is that the process data generated by the bump test be descriptive of actual process behavior. Discussed are the qualities required for “good” dynamic data and methods for modeling the dynamic data for controller design. Parameters from the dynamic model are not only used in correlations to compute tuning values, but also provide insight into controller design parameters such as loop sample time and whether dead time presents a performance challenge. It is becoming increasingly common for dynamic studies to be performed with the controller in automatic (closed loop). For closed loop studies, the dynamic data is generated by bumping the set point. The method for using closed loop data is illustrated. Concepts in this work are illustrated using a level control simulation.

Demystifying Performance Assessment Techniques

Real-time performance monitoring to identify poorly or under-performing loops has become an integral part of preventative maintenance. Among others, rising energy costs and increasing demand for improved product quality are driving forces. Automatic process control solutions that incorporate real-time monitoring and performance analysis are fulfilling this market need. While many software solutions display performance metrics, however, it is important to understand the purpose and limitations of the various performance assessment techniques since each metric signifies very specific information about the nature of the process. This paper reviews performance measures from simple statistics to complicated model-based performance criteria. By understanding the underlying concepts of the various techniques, readers will gain an understanding of the proper use of performance criteria. Basic algorithms for computing performance measures are presented using example data sets. An evaluation of techniques with tips and suggestions provides readers with guidance for interpreting the results.

Modeling Non-Steady State Data for PID Controller Tuning in a Cogeneration Power Plant

A 25 MW combined-cycle cogeneration plant at the University of Connecticut supplies electricity to the entire UConn campus with three natural gas combustion turbine generators and one high pressure steam turbine generator. Low pressure steam is used to provide building heat in the winter and to drive refrigeration compressors for chilled water cooling in the summer. The UConn Cogen plant is not permitted to charge for power it exports to the grid. All imported power cost the University the same as any large utility customer. The automatic control system thus seeks to operate this power plant while constantly fluctuating demand competes with the desire to maintain zero import and zero export of electric power. The highly integrated natural of the thermal cycles in the Cogen plant makes the concept of steady state operation a fleeting occurrence. Yet modern PID loop tuning tools suggest that a measured process variable (PV) should first be steadied before it is bumped so a dynamic controller output (CO) to PV relationship (i.e. dynamic process model) can be established for reliable PID loop tuning.

Utilizing Power Spectrum to Isolate Persistent Control Problems and to Improve Plant Profitability

Control problems range from malfunctioning valves to clogged heat exchangers along with most everything in between. Identifying such problems efficiently and isolating their source is typically a tedious task. This is especially true as production and maintenance staff find themselves faced with shifting priorities and mounting responsibilities that demand more and more of their attention. With heightened regulatory oversight and increased global competition pushing manufacturing to its limits, there is great need for correcting these problems quickly. For those that can correct these issues systematically, there are significant benefits to be gained.

Anticipating Equipment Failure Using Predictive Analytics

Asset maintenance and reliability are topics regularly covered in trade publications, around the water cooler, and in budget planning sessions. For those that walk the plant floor, however, those same terms are more often than not a reminder of a jarring phone call at 2:00 AM or a mid-day emergency stoppage associated with some catastrophic equipment failure. Anxiety from those experiences is partially tied to the knowledge that unplanned maintenance and repair costs are disproportionately high – as much as five times higher than planned maintenance. Those same experiences also came with a heightened sense of pressure and a corresponding risk of personal injury. Today’s production staff are caught in a Catch 22 when it comes to asset maintenance and reliability. Even though plant resources have become scarce, plant management’s expectations for production and profitability have increased.