Drought and grazing combined: Contrasting shifts in plant interactions at species pair and community level

The combined effects of drought stress and grazing pressure on shaping plant–plant interactions are still poorly understood, while this combination is common in arid ecosystems. In this study we assessed the relative effect of grazing pressure and slope aspect (drought stress) on vegetation cover and soil functioning in semi-arid Mediterranean grassland–shrublands in southeastern Spain. Moreover, we linked these two stress factors to plant co-occurrence patterns at species-pair and community levels, by performing C-score analyses. Vegetation cover and soil functioning decreased with higher grazing pressure and more south-facing (drier) slopes. At the community level, plants at south-facing slopes were negatively associated at no grazing but positively associated at low grazing pressure and randomly associated at high grazing pressure. At north-facing slopes, grazing did not result in a shift in the direction of the association. In contrast, analysis of pairwise species co-occurrence patterns showed that the dominant species Stipa tenacissima and Anthyllis cytisoides shifted from excluding each other to co-occurring with increasing grazing pressure at north-facing slopes. Our findings highlight that for improved understanding of plant interactions along stress gradients, interactions between species pairs and interactions at the community level should be assessed, as these may reveal contrasting results.

Rigorous design of distillation columns using surrogate models based on Kriging interpolation

The economic design of a distillation column or distillation sequences is a challenging problem that has been addressed by superstructure approaches. However, these methods have not been widely used because they lead to mixed-integer nonlinear programs that are hard to solve, and require complex initialization procedures. In this article, we propose to address this challenging problem by substituting the distillation columns by Kriging-based surrogate models generated via state of the art distillation models. We study different columns with increasing difficulty, and show that it is possible to get accurate Kriging-based surrogate models. The optimization strategy ensures that convergence to a local optimum is guaranteed for numerical noise-free models. For distillation columns (slightly noisy systems), Karush–Kuhn–Tucker optimality conditions cannot be tested directly on the actual model, but still we can guarantee a local minimum in a trust region of the surrogate model that contains the actual local minimum.

Optimization of Chemical Processes Using Surrogate Models Based on a Kriging Interpolation

Superstructure approaches are the solution to the difficult problem which involves the rigorous economic design of a distillation column. These methods require complex initialization procedures and they are hard to solve. For this reason, these methods have not been extensively used. In this work, we present a methodology for the rigorous optimization of chemical processes implemented on a commercial simulator using surrogate models based on a kriging interpolation. Several examples were studied, but in this paper, we perform the optimization of a superstructure for a non-sharp separation to show the efficiency and effectiveness of the method. Noteworthy that it is possible to get surrogate models accurate enough with up to seven degrees of freedom.

Large scale optimization of a sour water stripping plant using surrogate models

In this work, we propose a new methodology for the large scale optimization and process integration of complex chemical processes that have been simulated using modular chemical process simulators. Units with significant numerical noise or large CPU times are substituted by surrogate models based on Kriging interpolation. Using a degree of freedom analysis, some of those units can be aggregated into a single unit to reduce the complexity of the resulting model. As a result, we solve a hybrid simulation-optimization model formed by units in the original flowsheet, Kriging models, and explicit equations. We present a case study of the optimization of a sour water stripping plant in which we simultaneously consider economics, heat integration and environmental impact using the ReCiPe indicator, which incorporates the recent advances made in Life Cycle Assessment (LCA). The optimization strategy guarantees the convergence to a local optimum inside the tolerance of the numerical noise.