325928 Robust Process Modelling and Optimization Under Uncertainty

Thursday, November 7, 2013: 1:14 PM
Continental 4 (Hilton)
Usman Abubakar, Srinivas Sriramula and Neill C. Renton, University of Aberdeen, Aberdeen, United Kingdom

ROBUST PROCESS MODELLING AND OPTIMIZATION UNDER UNCERTAINTY

Usman Abubakar, Srinivas Sriramula and Neill C. Renton

Lloyd's Register Foundation (LRF) Centre for Safety & Reliability Engineering, University of Aberdeen, UK.

This paper presents a new stochastic module that can be integrated into traditional deterministic simulators to facilitate chemical process modelling, design, and operations under uncertainty. The paper shows how the proposed stochastic module could be employed to obtain a wide range of probabilistic process performance measures to support decisions allowing improvements in process robustness, cost efficiency, safety and reliability. The module can be applied to model performance behaviour of processes with implicit or unknown performance functions, linear or nonlinear responses, governed by Gaussian or non-Gaussian random variables. There is also a provision for both random and systematic sampling in the framework. Sample case studies have been performed to highlight the applicability of the new module, including the one described in Fig.1. It depicts a synthesis gas (syngas) production process, which is subject to input noise.

Figure 1: Modelling stochastic performance of syngas production process

Samples of sizes 1000, 2000, 10000 and 30000 were drawn from the assumed distributions of each of the uncertain variables and the effects of the random inputs are propagated across the process flow diagram using a traditional process simulation package. The sampling is automated so that a large number of random outputs corresponding to the uncertain inputs can be obtained typically in minutes.  The results are used to determine a limit state function, M = G(X), where X is the vector of the random inputs, M ≤ 0 is setup to split the process performance space into failure and success regions and M = 0 defines the failure boundary. The properties of the random variables are then used to determine the probability that P[M ≤ 0] , thus:

Equationnew.gif

Where fX(.) is the joint probability density function. In addition, process reliability/flexibility index, most probable design/operation condition, performance space charts, global sensitivity indices, etc, can be obtained from the module. Adding such capabilities to the traditional deterministic process simulators provides a simple method of generating probabilistic performance measures that can support chemical engineers as they seek to improve plant safety, reliability and financial performance.

 


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See more of this Session: Design and Operation Under Uncertainty II
See more of this Group/Topical: Computing and Systems Technology Division