Control strategies for ventilation networks in small‐scale mines using an experimental benchmark

Abstract

In view of the frequent ventilation network changes during production in underground mining, decreasing sensors and actuators without altering production control and safety is one of the chief engineering challenges. This work is focused on modeling identification and control strategies for underground ventilation networks in small‐scale mines using an experimental benchmark. Guidelines to obtain a discrete state space model are provided, considering the conservation laws in the network to define the structure of the linear model. The main purpose of the paper is to analyze the use of classic controllers in the mine ventilation system when there are limitations on the number of sensors and actuators available to design a feedback control system. A comparison of three classic control strategies is presented considering the a constraint on the available number of sensors. Experimental and simulation results are presented.

Physical Modeling and Structural Properties of Small-Scale Mine Ventilation Networks

“…The required airflow produced by the fans has an energy cost which is directly proportional to the friction opposing the passage of air. The friction depends on the size and number of mine branches and the characteristics of the interconnected nodes [18].…”

“…A small-scale test bench has been constructed at GIPSA-Lab to perform experimental evaluations on control strategies for ventilation networks on underground mines [18,34]. The benchmark set-up is shown in Figure 1.…”

Explicit Prediction-Based Control for Linear Difference Equations With Distributed Delays

“…In this paper, we consider such a Linear Difference Equation, with both pointwise and distributed delays, and subjected to a pointwise input delay. This specific type of difference system arises, for example, when controlling a network of hyperbolic PDEs, such as mining ventilation systems [22] or oil production systems consisting of networks of pipes [23]. When control is applied at the boundary of one of the PDEs subsystems, it will act on distal PDE subsystems through proximal ones, resulting in a pointwise input delay.…”

Stabilization of a hyperbolic PDEs-ODE network using a recursive dynamics interconnection framework

“…Interconnected systems represented by Partial Differential Equations (PDEs) naturally appear when modeling industrial processes. Some well-known examples include traffic network systems with different types of vehicles [15], [46], ventilation in buildings [35], density-flow systems [10], [25], open channels [22], [23], [24], communication networks [19]. Additional couplings with Ordinary Differential Equations (ODEs) arise when there is a lumped element coupled to the distributed dynamics such as in heavy chain systems [34], Rijke tubes [17] (where the ODE is sandwiched between two PDEs) or mechanical vibrations in drilling applications [36].…”