Self-optimizing control provides nearly optimal operation of process systems in the face of varying disturbances, where the inputs are adjusted to hold selected controlled variables (c) at constant setpoints. It is possible to have better self-optimizing properties by controlling linear combinations of measurements (c = Hy) than by controlling individual measurements. Previous work focused on selecting combination matrix H to minimize worst-case loss, that arises due to the use of suboptimal self-optimizing control policy. In this paper, we present a method for finding combination matrix H that minimizes average loss for local self-optimizing control. It is further shown that the combination matrix that minimizes average loss is superoptimal in the sense that it also minimizes worst-case loss simultaneously. The usefulness of the results is demonstrated using an evaporator case study.

The Principal Component Analysis (PCA) and the Partial Least Squares (PLS) are two commonly used techniques for process monitoring. Both PCA and PLS assume that the data to be analysed are not self-correlated i.e. time-independent. However, most industrial processes are dynamic so that the assumptions of time-independence made by the PCA and the PLS are invalid in nature. Dynamic extensions to PCA and PLS, so called DPCA and DPLS, have been developed to address this problem, however, unsatisfactorily. Nevertheless, the Canonical Variate Analysis (CVA) is a state-space based monitoring tool, hence is more suitable for dynamic monitoring than DPCA and DPLS. The CVA is a linear tool and traditionally for simplicity, the upper control limit (UCL) of monitoring metrics associated with the CVA is derived based on a Gaussian assumption. However, most industrial processes are non-linear and the Gaussian assumption is invalid for such processes so that CVA with a UCL based on this assumption may not be able to correctly identify underlying faults. In this work, a new monitoring technique using the CVA with UCLs derived from the estimated probability density function through kernel density estimations (KDE) is proposed

Selection of controlled variables (CVs) has recently gained wide attention, because of its paramount importance in real-time optimization (RTO) of plant operation. The so-called self-optimizing control (SOC) strategy aims to select appropriate CVs so that when they are maintained at constant setpoints, the overall plant operation is optimal or near optimal, despite various disturbances and uncertainties. Recent progresses of the SOC methodology have focused on finding linear combinations of measurements as CVs via linearization of the process around its nominal operating point, which results in the plant operation being only locally optimal. In this work, the concept of necessary conditions of optimality (NCO) is incorporated into CV selection to overcome the "local" shortcoming of existing SOC methods. Theoretically, the NCO should be selected as the optimal CV, although it may not be practical because of the measurability of the NCO. To address this issue, in this work, CVs are selected to approximate unmeasured NCO over the entire operation region with zero setpoints to achieve near-optimal operation globally. The NCO approximation CVs can be obtained through any existing regression approaches. Among them, two particular regression methodsnamely, least-squares and neural networksare adopted in this work as an illustration of the proposed methodology. The effectiveness and advantages of the new approach are demonstrated through two case studies. Results are compared with those obtained by using existing SOC methods and an NCO tracking technique.

Early detection of incipient faults in industrial processes is increasingly becoming important, as these faults can slowly develop into serious abnormal events, an emergency situation, or even failure of critical equipment. Multivariate statistical process monitoring methods are currently established for abrupt fault detection. Among these, canonical variate analysis (CVA) was proven to be effective for dynamic process monitoring. However, the traditional CVA indices may not be sensitive enough for incipient faults. In this work, an extension of CVA, called the canonical variate dissimilarity analysis (CVDA), is proposed for process incipient fault detection in nonlinear dynamic processes under varying operating conditions. To handle non-Gaussian distributed data, kernel density estimation was used for computing detection limits. A CVA dissimilarity-based index has been demonstrated to outperform traditional CVA indices and other dissimilarity-based indices, namely DISSIM, RDTCSA, and GCCA, in terms of sensitivity when tested on slowly developing multiplicative and additive faults in a CSTR under closed-loop control and varying operating conditions.

a b s t r a c tIndustrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/down load.html〉 Accessed 21.03.2014).The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms.

No abstract

Incipient fault monitoring is becoming very important in large industrial plants, as the early detection of incipient faults can help avoid major plant failures. Recently, Canonical Variate Dissimilarity Analysis (CVDA) has been shown to be an efficient technique for incipient fault detection, especially under dynamic process conditions. CVDA can be extended to nonlinear processes by introducing kernel-based learning. Incipient fault monitoring requires kernels with both good interpolation and extrapolation abilities. However, conventional single kernels only exhibit one ability or the other, but not both. To overcome this drawback, this study presents a Mixed Kernel CVDA method for incipient fault monitoring in nonlinear dynamic processes. Due to the use of mixed kernels, both enhanced detection sensitivity and a better depiction of the growing fault severity in the monitoring charts are achieved. Looking ahead, this work takes a step towards understanding the impact of kernel behavior in process monitoring performance.

Self-optimizing control (SOC) constitutes an important class of control strategies for real-time optimization (RTO) of chemical plants, by means of selecting appropriate controlled variables (CVs). Within the scope of SOC, this paper develops a CV selection methodology for a global solution which aims to minimize the average economic loss across the entire operation space. A major characteristic making the new scheme different from existing ones is that each uncertain scenario is independently considered in the new solution without relying on a linearized model, which was necessary in existing local SOC methods. Although global CV selection has been formulated as a nonlinear programming (NLP) problem, a tractable numerical algorithm for a rigorous solution is not available. In this work, a number of measures are introduced to ease the challenge. First, we suggest representing the economic loss as a quadratic function against the controlled variables through Taylor expansion, such that the average loss becomes an explicit function of the CV combination matrix, and a direct optimizing algorithm is proposed to approximately minimize the global average loss. Furthermore, an analytic solution is derived for a suboptimal but much more simplified problem by treating the Hessian of the cost function over the entire operating space as a constant. This approach is found to be very similar to one of the existing local methods, except that a matrix involved in the new solution is constructed from global operating data instead of using a local linear model. The proposed methodologies are applied to two simulated examples, where the effectiveness of the proposed algorithms is demonstrated.