Optimized model-based control of main mine ventilation air flows with minimized energy consumption

In early 2018,the Boliden Garpenberg operation implemented an optimized control strategy as an addition to the existing ventilation on demand system.The purpose of the strategy is to further minimize energy use for main and booster fans,whilst also fulfilling airflow setpoints without violating constraints such as min/max differential pressure over fans and interaction of air between areas in mines.Using air flow measurements and a dynamical model of the ventilation system,a mine-wide coordination control of fans can be carried out.The numerical model is data driven and derived from historical operational data or step changes experiments.This makes both initial deployment and lifetime model maintenance,as the mine evolves,a comparably easy operation.The control has been proven to operate in a stable manner over long periods without having to re-calibrate the model.Results prove a 40%decrease in energy use for the fans involved and a greater controllability of air flow.Moreover,a 15%decrease of the total air flow into the mine will give additional proportional heating savings during winter periods.All in all,the multivariable controller shows a correlation between production in the mine and the ventilation system performance superior to all of its predecessors.

A review of optimization methods for computation offloading in edge computing networks

Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational problem.Edge Computing is an emerging computation paradigm that is employed to conquer this problem.It can bring computation power closer to the end devices to reduce their computation latency and energy consumption.Therefore,this paradigm increases the computational ability of SMDs by collaboration with edge servers.This is achieved by computation offloading from the mobile devices to the edge nodes or servers.However,not all applications benefit from computation offloading,which is only suitable for certain types of tasks.Task properties,SMD capability,wireless channel state,and other factors must be counted when making computation offloading decisions.Hence,optimization methods are important tools in scheduling computation offloading tasks in Edge Computing networks.In this paper,we review six types of optimization methods-they are Lyapunov optimization,convex optimization,heuristic techniques,game theory,machine learning,and others.For each type,we focus on the objective functions,application areas,types of offloading methods,evaluation methods,as well as the time complexity of the proposed algorithms.We discuss a few research problems that are still open.Our purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks.