Mine dewatering can represent up to 5 % of the total energy demand of a mine, and is one of the mine systems that aim to guarantee safe operating conditions. As mines go deeper, dewatering pumping heads become greater, potentially involving several lift stages. Greater depth does not only mean greater dewatering cost, but more complex systems that may require more sophisticated control systems, especially if mine operators wish to gain benefits from demand response incentives that are becoming a routine part of electricity tariffs.
This work explores a two stage economic optimization procedure of an underground mine dewatering system, consisting of two lifting stages, each one including a pump station and a water reservoir. First, the system design is optimized considering hourly characteristic dewatering demands for twelve days, one day representing each month of the year. This optimization is based on an annuitized value of the operation cost of the system, and therefore includes the investment costs in pumps and underground reservoirs. Reservoir size and pump capacity, as well as a pumping operation plan are calculated, defined by characteristic hourly electricity prices and water inflows (seepage and water use from production activities), at best known through historical observations for the previous year. There is no guarantee that the system design will remain optimal when it faces the water inflows and market determined electricity prices of the year ahead, or subsequent years ahead, because these remain unknown at design time. Consequently, the optimized dewatering system design is adopted as part of a Model Predictive Control (MPC) strategy that adaptively maintains optimality in operations, to deal with uncertainty in the future operating conditions.
By means of simulation, the relative benefits of use of a centralized MPC strategy and a distributed MPC strategy are explored. Results show that the system can be reliably controlled using either of the control strategies proposed. Under the operating conditions considered, cost savings for non-optimized designs (to represent existing dewatering systems in mines) are as great as 4.2% for both strategies, but are higher when the system design is improved via life cycle cost optimization.