Specifying the directionality of fault propagation paths using transfer entropy

July 4-7 200

Closed loop identification based on quantization

This paper proposes a new closed-loop identification scheme for a single-input-single-output control loop. It is based upon a quantizer inserted into the feedback path. The quantizer can be used to generate an equivalent persistently exciting signal with which the well known two-stage and/or two-step method can be used directly. Simulation examples and an experimental demonstration are used to illustrate the proposed scheme

Performance assessment and diagnosis of refinery control loops

This paper discusses the application of control loop performance assessment (Desborough and Harris, 1992) in a refinery setting. In a large process it is not feasible to tailor the parameters of the algorithm to every individual control loop. A procedure is illustrated for selecting default values which make it possible to implement the technology on a refinery-wide scale. For instance, it is shown that the prediction horizon perameter in the CLPA algorithm can be set so that the analysis is sensitive to the persistent signals that cause loss of performance. Default values are suggested for refinery applications.A frequent cause of loss of performance in a control loop is a persistent oscillation due to a valve nonlinearity or a tuning fault. The paper presents an operational signatures in the form of an estimate of the closed loop impulse response that suggest the causes of such oscillations

Finding the direction of disturbance propagation in a chemical process using transfer entropy

Published versio

Optimal selection of control structure using a steady-state inversely controlled process model

The profitability of chemical processes strongly depends on their control systems. The design of a control system involves selection of controlled and manipulated variables, known as control structure selection. Systematic generation and screening alternative control structures requires optimization. However, the size of such an optimization problem is much larger when candidate controllers and their parameters are included and it rapidly becomes intractable. This paper presents a novel optimization framework using the notion of perfect control, which disentangles the complexities of the controllers. This framework reduces the complexity of the problem while ensuring controllability. In addition, the optimization framework has a goal-driven multi-objective function and requires only a steady-state inverse process model. Since dynamic degrees of freedom do not appear in a steady-state analysis, engineering insights are employed for developing the inventory control systems. The proposed optimization framework was demonstrated in a case study of an industrial distillation train

Integrated design and control using a dynamic inversely controlled process model

The profitability of chemical processes depends on their design and control. If the process design is fixed, there is little room left to improve control performance. Many commentators suggest design and control should be integrated. Nevertheless, the integrated problem is highly complex and intractable. This article proposes an optimization framework using a dynamic inversely controlled process model. The combinatorial complexities associated with the controllers are disentangled from the formulation, but the process and its control structure are still designed simultaneously. The new framework utilizes a multi-objective function to explore the trade-off between process and control objectives. The proposed optimization framework is demonstrated on a case study from the literature. Two parallel solving strategies are applied, and their implementations are explained. They are dynamic optimization based on (i) sequential integration and (ii) full discretization. The proposed integrated design and control optimization framework successfully captured the trade-off between control and process objectives

Rogue seasonality detection in supply chains

Rogue seasonality or unintended cyclic variability in order and other supply chain variables is an endogenous disturbance generated by a company’s internal processes such as inventory and production control systems. The ability to automatically detect, diagnose and discriminate rogue seasonality from exogenous disturbances is of prime importance to decision makers. This paper compares the effectiveness of alternative time series techniques based on Fourier and discrete wavelet transforms, autocorrelation and cross correlation functions and autoregressive model in detecting rogue seasonality. Rogue seasonalities of various intensities were generated using different simulation designs and demand patterns to evaluate each of these techniques. An index for rogue seasonality, based on the clustering profile of the supply chain variables was defined and used in the evaluation. The Fourier transform technique was found to be the most effective for rogue seasonality detection, which was also subsequently validated using data from a steel supply network

A change in the NICE guidelines on antibiotic prophylaxis

Since 2008, NICE clinical guidelines have stated: ‘Antibiotic prophylaxis against infective endocarditis is not recommended for people undergoing dental procedures’. This put UK guidance at odds with guidance in the rest of the world, where antibiotic prophylaxis is recommended for patients at high-risk of infective endocarditis undergoing invasive dental procedures. Many dentists also felt this wording prohibited the use of antibiotic prophylaxis, regardless of the wishes of the patient or their personal risk of infective endocarditis and made it difficult for them to use their clinical judgment to deliver individualised care in the best interests of their patients. NICE have now changed this guidance to ‘Antibiotic prophylaxis against infective endocarditis is not recommended routinely for people undergoing dental procedures.’ This article examines the implications of this small but important change