Multiobjective nonlinear model predictive control of forestry problems

The use of rigorous computational tools is necessary to control forest pollution and minimize carbon dioxide emissions. In this work, a rigorous multi objective nonlinear model predictive control strategy is adopted on three different forestry models. The optimization was performed with the optimization language PYOMO in conjunction with the state-of-the-art optimization solvers IPOPT. The globality of the solutions was confirmed with the global optimization solver BARON. The optimum profiles generated show that this strategy is effective in minimizing the carbon dioxide emissions, and forest pollutants maximizing the forest biomass density. The control of non-wood-based industrial activity is beneficial to keep the depletion of forestry as low as possible and minimize the emission of unwanted carbon dioxide into the atmosphere.