Offline tools for on-site control implementation
Application of productivity tools and work process changes can expedite advanced control projects and reduce effort in refining applications
Ken Allsford, Bhaskar Iyer and Aric Tomlins
Advanced Solutions, Honeywell International
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Process control engineers are always challenged to be more effective, to successfully commission new, advanced regulatory control strategies and multivariable control applications. To address the challenge, the practitioner needs to continuously improve and apply an understanding of both control technology in general and the process technology specifics relevant to each control project. This article discusses some of the newer productivity tools and work process approaches that are now becoming more widely deployed.
The development programs of control product software vendors continuously improve core and auxiliary products. Although one of the outcomes of these programs is a migration to product suites, with user interfaces based on a workflow perspective, there is still a vast range of “designed-in” capability and functionality. Also, as knowledge and experience filter through the user community, the practical consequences of software enhancements are that improvements to product features enable capabilities that are beyond the original implementation rationale. These capabilities also have to be understood and internalised. Software product improvements are also obviously tied to particular releases. In combination, these factors lead to the challenge of minimising the time to modify and update control project work processes to fully incorporate efficiencies facilitated by software product improvements.
As well as establishing effective general control project work processes, the practitioner is further challenged to be specifically efficient in regards to the execution of each and every project. Practically, this requires the practitioner to develop a deep understanding of the actual process unit(s) relevant to the control project. And this needs to happen over a relatively short duration. Early understanding helps to filter inputs such as comments from operations and to identify upfront any issues that may challenge project success. Early understanding also enables better definition of the control problem and, as such, selection of an appropriate solution from among the available options. These activities have a parallel in terms of the front-end engineering design (FEED) phase typical for engineering projects in the process industries. On completion of the design and selection of the solution to the control problem, the project moves on to implementation. This corresponds to the detailed design, construction and commissioning phase typical for engineering projects in the process industries. A primary technical objective here is the identification of the relationships of the independent variable(s) (manipulated and disturbance variables, MV and DV) to the dependent variable(s) (controlled variables, CV). A secondary technical objective is the discovery of reasonable control application tuning parameters given the selected control product technology. During implementation, the practitioner is once again challenged to continue to be effective.
Integration of the knowledge and experience of plant operations and engineering personnel into the selection and implementation of the control problem solution has always been integral to the success of a controls project. The core aspect of this knowledge and experience can be viewed as tacit knowledge. This is knowledge that is neither expressed nor declared openly, but rather implied or simply understood and is often associated with intuition. As such, the capture of this knowledge is not well defined —control project work processes usually deploy both structured interviews and ad hoc discussions as tools to help in this knowledge transfer to the control project team. This knowledge transfer is primarily aimed at the higher level of process understanding and the initial definition of the control problem solution (in terms of CV, MV and DV). It is usually done in conjunction with historical data review, including visualisation, regression analysis and use of general spreadsheet-oriented tools.
The goal of advanced process control is successful projects leading to a competitive advantage for the organisation through superior process operations. Roll-out was done by engineers, many renown and with a background in chemical engineering. As such, toolkits based on process engineering models were sometimes developed to support this activity. This was particularly the case for refining and other large-scale petrochemical processes. These toolkits were typically developed initially with the limited focus of providing output just for process control.
Examples include toolkits for fractionators (based on distillation principles), delayed coking and platforming. The degree of technology openness and toolkit productisation varies. Commercialisation of such toolkits represents a challenge, as often a toolkit is focused on a particular process unit and there are only so many installations of that process unit worldwide. Where available and maintained, these toolkits remain a key component of the control solution for certain processes. However, the focus and narrative of control product software vendors moved relatively quickly towards data-centric modelling and efficiency tools that have broad applicability to all control projects.
The earliest advanced control software products included control model identification tools. The tools analyse plant data, and a key output is statistically validated time-domain relationships between independent and dependent variables. Relationships of independent to dependent variables are generally sought in terms of a linear steady-state gain and some definition of the time-dependent path to steady state. Generally, accuracy for the former is prioritised over accuracy for the latter, as (relative) steady-state gains are used for both control and economic optimisation, and can also be used in validating the viability of the selected control solution. In addition, the tacit knowledge transfer between process operations and the control project team discussed above is often more successful for some general aspects of the dynamic relationship (such as dead time and first-order time constant) than for steady-state gain.
Initially, these tools were not integrated with their input data generation, and control practitioner organisations would emphasise their work practices that enable efficient collection of this input data with minimum explicit effort and disruption to the process unit. Nevertheless, in some circles, the mindset was that identification was a very costly process with long elapsed times. And this is not to mention the impact of changes on controller design as a consequence of a revised requirements definition or increased understanding of the best solution to the control problem.
Control product vendors responded by integrating the data generation step with the identification step in real time. Indeed, these tools have received significant write-up in the technical press, including many documented successes. The first generation of such tools required the process that is being identified to be operated in open loop while identification is performed by stepping the process. In practice, this leads to the selection of small steps and the identification on small sub-groups of independent and dependent variables at a time, in order to minimally interfere with the process operational goals.
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