A service reliability model predictive control with dynamic safety stocks and actuators health monitoring for drinking water networks

Abstract

Abstract-This paper presents a model predictive control strategy to assure reliability in drinking water networks given a customer service level and a forecasting demand. The underlying idea concerns a two-layer hierarchical control structure. The upper layer performs a local steady-state optimization to set up an inventory replenishment policy based on dynamic safety stocks for each tank in the network. At the same stage, actuators health is revised to set up their next maximum allowable degradation in order to efficiently distribute overall control effort and guarantee system availability. In the lower layer, a model predictive control algorithm is implemented to compute optimal control set-points to minimize a multiobjective cost function. Simulation results in the Barcelona drinking water network have shown the effectiveness of the dynamic safety stocks allocation and the actuators health monitoring to assure service reliability and optimizing network operational costs.

Constrained distributed optimization: A population dynamics approach

“…There might be alternative posibilities in the selection of the mass dynamics (7). However, the requirements that should be satisfied are: i) the dynamics satisfy the communication constraints established by the graph G, and ii) dynamics converge to the equilibrium point given by the following equality:…”

Reliability–based economic model predictive control for generalised flow–based networks including actuators’ health–aware capabilities

“…This approach was proposed by Grosso et al (2012). It uses the original output bounds, but incorporates a dynamic state soft constraint to guarantee a desired service level under demand uncertainty.…”

“…The main contribution of this paper consists in an improved reliability-based economic MPC strategy that is aware of the actuator health and allows dynamic management of risk for non-stationary demand uncertainty, extending the results presented by Grosso et al (2012;. Specifically, the two-layer control architecture proposed by Grosso et al (2012) is here simplified and reduced to a less conservative single-layer stochastic approach following the chance-constrained MPC approach presented by Grosso et al (2014).…”

“…Specifically, the two-layer control architecture proposed by Grosso et al (2012) is here simplified and reduced to a less conservative single-layer stochastic approach following the chance-constrained MPC approach presented by Grosso et al (2014). The actuator-health management policy used in this paper follows the one introduced by Pereira et al (2010), but considers stochastic actuator-degradation models and probabilistic actuator lifetime constraints rather than deterministic ones.…”

“…Therefore, it is desired to have a flexible control strategy that allows a trade-off between the economic optimisation and the reliability of the system. To achieve this aim, only a two-layer hierarchical control strategy has been proposed by Grosso et al (2012) for network flow optimisation considering both economic and reliability criteria. In such a work, first an upper layer performs a local steady-state economic optimisation to set up a uniform back-off of a demand satisfaction constraint due to an assumption of stationary demand uncertainty.…”