Stochastic Procedures for the Optimal Sensor Location in Chemical Plants

CARNERO MERCEDES; HERNÁNDEZ JOSÉ; SANCHEZ MABEL

San Francisco

Congreso; 2013 AIChE Annual Meeting; 2013

Process information is the foundation upon which monitoring, control, optimization, planning and scheduling, fault diagnosis, etc. are based. To satisfy information equirements, regarding its quality and availability, it is essential to locate an appropriate sensor network (SN) in the plant. The SN designer should decide to measure each process variable or not. These decisions are mathematically formulated in terms of binary variables. A combinatorial optimization problem results that is usually multimodal and involves many binary variables. Its solution has been addressed using tree search algorithms, MILP techniques and stochastic procedures. Regarding the stochastic solution schemes, techniques based on Genetic Algorithms (GAs) were proposed initially. Recently a metaheuristic approach based on the Estimation of Distribution Algorithms (EDAs) and SOTS was presented (Carnero et al., 2013). Application results of that procedure demonstrated that the combination of EDAs and SOTS advantages has a synergistic effect on the solution of the SN design problem (SNDP). The proposed solution scheme makes use of the Population Based Incremental Learning Algorithm (PBIL) developed by Baluja (1994), which assumes independent relationships among variables. In this work, the utilization of probabilistic models that capture variable interdependencies, such as the marginal product factorization model (Santana et al., 2010), to solve the SNDP is analysed. A comparative performance study is conducted to evaluate the benefits of increasing the complexity of the distribution model.