Optimal Sensor Location in Chemical Plants Using the Estimation of Distribution Algorithms

Abstract

The optimal selection of sensor structures improves the knowledge of the current plant state, which is a central issue for the decision-making process. Instrumentation design is a challenging optimization problem that involves a huge amount of binary variables that represent the possible sensor locations. In this work, the limitations of the current design strategies are discussed, and they support the application of evolutionary solution methods. Among them, the estimation of distribution algorithms (EDAs) arises as a convenient alternative to solving the problem. These are stochastic optimization strategies devised to capture complex interactions among problem variables by learning the probabilistic model of candidate solutions and its sampling to generate the next population. From the broad spectrum of EDAs that use multivariate models, two representative procedures are selected that significantly differ in the methods used for learning and sampling those models. Furthermore, a comparative performance study is conducted to evaluate the benefits of increasing the complexity of the distribution model with respect to a memetic procedure based on univariate models.

Tuning a hybrid SA based algorithm applied to Optimal Sensor Network Design

“…In this section, we compare the behavior of the best algorithmic variants of HSA versus other well-known algorithm found in the literature for solving these SNDP case studies, in order to answer the RQ3. In this sense PBIL SOTS, introduced by Carnero et al [5], has recently reported results for these cases.…”

“…Exponential Scheme. The exponential cooling scheme produces the temperature decay by applying the Eq (4), where the constant α k < 1 is calculated as presented in the Eq (5). This schedule quickly cools the temperature reducing the required time and iterations to converge to a good solution.…”

“…The standard deviation of flow meters is 2.5%, 1%, 2%, 2%, and 2% of the corresponding true flow rates for case studies 1-5, respectively. The complexity of the set of constraints imposed on all case studies can be found in [5].…”

“…The literature presents genetic algorithms [3], swarm intelligence [4], among others population-based metaheuristics. Furthermore, hybridized metaheuristics have been reported to solve this problem, Carnero et al [5] propose an algorithm called PBIL SOTS, which combines estimation of distribution algorithm with a tabu search improved by using a strategic oscillation.…”

Tighter Mixed-Integer Quadratic Programming Model for Process Data Rectification

“…In this section, all of the evaluated MIP methods, i.e., MILP, MIQP, MT-MIQP, MIQP-MT, and MIQP-NT, were applied to a case used by Carnero et al, 40 which involves 47 units connected with 82 streams. The detailed information of the tested case is taken from https://www.ing.unrc.edu.ar/ archivos/sndp_cases.doc and can also be seen in the Supporting Information.…”