DuyQuang Nguyen

Optimization of preventive maintenance scheduling in processing plants

Abstract A new methodology designed to optimize both the planning of preventive maintenance and the amount of resources needed to perform maintenance in a process plant is presented. The methodology is based on the use of a Montecarlo simulation to evaluate the expected cost of maintenance as well as the expected economic loss, an economical indicator for maintenance performance. The Montecarlo simulation describes different failure modes of equipment and uses the prioritization of maintenance supplied, the availability of labour and spare parts. A Genetic algorithm is used for optimisation. The well-known Tennessee Eastman Plant problem is used to illustrate the results.

Stochastic-based accuracy of data reconciliation estimators for linear systems

Abstract Accuracy of an instrument has been traditionally defined as the sum of the precision and the bias. Recently, this notion was generalized to estimators [Bagajewicz, M. (2005a). On the definition of software accuracy in redundant measurement systems. AIChE Journal , 51 (4), 1201–1206]. The definition was based on the maximum undetected bias and ignored the frequency of failure, thus providing an upper bound. In more recent work [Bagajewicz, M. (2005b). On a new definition of a stochastic-based accuracy concept of data reconciliation-based estimators. In European Symposium on Computer-Aided Process Engineering Proceeding (ESCAPE) ], a more realistic concept of expected value of accuracy was presented. However, only the timing of failure and the condition of failure was sampled. In this paper we extend the Monte Carlo simulations to also sample the size of the gross errors and we provide new insights on the evolution of biases through time.

On the impact of sensor maintenance policies on stochastic-based accuracy

The concept of stochastic-based accuracy and its calculation procedure for the case of corrective maintenance in data reconciliation-based systems were recently presented [Bagajewicz, M. (2005b). On a new definition of a stochastic-based accuracy concept of data reconciliation-based estimators. In: Proceedings of the 15th European symposium on computer-aided process engineering; Bagajewicz, M., & Nguyen, D. (2008). Stochastic-based accuracy of data reconciliation estimators for linear systems. Computers and Chemical Engineering, 32(6), 1257–1269]. This paper discusses the effect of preventive maintenance policies in chemical plants on stochastic-based accuracy. We show and evaluate a few practical solutions to the problem. The extent to which an effective maintenance policy helps to improve accuracy is shown and the economic justification is provided. Two examples are provided.

Parallel computing approaches to sensor network design using the value paradigm

One previous paradigm was based on sensor positioning selection to allow key variable observability. Later, the problem was posed as a minimum cost problem subject to sensor network performance (precision and gross error robustness first and accuracy as a replacement later). In a previous paper (Nguyen and Bagajewicz, submitted for publication), we presented a new paradigm in sensor network design, one that maximizes value, defined as economic value of information minus its cost. We presented two methods to solve the problem and we concluded that their performance was acceptable for small and medium size problems, but not for large problems. In this paper we use parallel computing approaches for large problems.

New method for sensor network design and upgrade for optimal process monitoring

Abstract Previous methods on nonlinear sonsor network design minimize cost subject to a variety of constraints linked to the network performance: precision, residual precision, error detectability and resilience. In recent work, the use of accuracy as an attribute of a network that can replace precision, error detectability and resilience more effectively has been considered. In this paper, we propose a sensor network design methodology based on accuracy thresholds.