11h30 - 11h55
Robust real-time optimization for blending operation of alumina production
The blending operation is a key process in alumina production. The real-time optimization (RTO) of finding an optimal raw material propor- tioning is crucially important for achieving the desired quality of the product. However, the presence of uncertainty is unavoidable in a real process, lead- ing to much difficulty for making decision in real-time. This paper presents a novel robust real-time optimization (RRTO) method for alumina blending operation, where no prior knowledge of uncertainties is needed to be utilized. The robust solution obtained is applied to the real plant and the two-stage operation is repeated. When compared with the previous intelligent optimiza- tion (IRTO) method, the proposed two-stage optimization method can better address the uncertainty nature of the real plant and the computational cost is much lower. From practical industrial experiments, the results obtained show that the proposed optimization method can guarantee that the desired quality of the product quality is achieved in the presence of uncertainty on the plant behavior and the qualities of the raw materials. This outcome suggests that the proposed two-stage optimization method is a practically significant approach for the control of alumina blending operation.
11h55 - 12h20
Optimal Path Finding in Urban Operations
Emergencies operations can occur in confined urban buildings. Urban operation units often encounter mobile and static obstacles, and must find in real time the optimal path to handle such complicated environment. This presentation gives an agent-based path-finding algorithm for real-time realization. Reducing repetitive computation is achieved by classifying the obstacles into clusters. Agent-based classifiers allow parallel computing to improve operation units’ response. Consensus among multiple obstacle perspectives can lead to an exhaustive set of all possible clusters. The problem is addressed by introducing probability-triggering conditions to a dynamic programming approach to find the desired paths in a confined area.
12h20 - 12h45
Event-Triggered Hybrid Consensus for Multi-Agent Networks With Directed Topologies
This paper will introduce an event-triggered hybrid control technique for the multi-agent network consensus problem with directed topologies and pull-based setup. The hybrid controller consists of an event-triggered feedback controller and an impulsive state update rule. The feedback control renews agents' feedback information at the event times and the impulsive control rule updates those agents' system states at the impulsive times. We first derive general event-triggered hybrid principles. The results are hence reduced to synchronous hybrid principles, where all impulsive times coincide with the event times. Numerical examples provide evidences that the hybrid control strategy can exhibit a better convergence performance than pure event-triggered control.