Soil quality response to land-use change for sugarcane expansion in Brazil

Globally, increasing demands for biofuels have intensified the rate of land-use change (LUC) for expansion of bioenergy crops. In Brazil, the world\'s largest sugarcane-ethanol producer, sugarcane area has expanded by 35% (3.2 Mha) in the last decade. Sugarcane expansion has resulted in extensive pastures being subjected to intensive mechanization and large inputs of agrochemicals, which have direct implications on soil quality (SQ). We hypothesized that LUC to support sugarcane expansion leads to overall SQ degradation. To test this hypothesis we conducted a field-study at three sites in the central-southern region, to assess the SQ response to the primary LUC sequence (i.e., native vegetation to pasture to sugarcane) associated to sugarcane expansion in Brazil. At each land use site undisturbed and disturbed soil samples were collected from the 0-10, 10-20 and 20-30 cm depths. Soil chemical and physical attributes were measured through on-farm and laboratory analyses. A dataset of soil biological attributes was also included in this study. Initially, the LUC effects on each individual soil indicator were quantified. Afterward, the LUC effects on overall SQ were assessed using the Soil Management Assessment Framework (SMAF). Furthermore, six SQ indexes (SQI) were developed using approaches with increasing complexity. Our results showed that long-term conversion from native vegetation to extensive pasture led to soil acidification, significant depletion of soil organic carbon (SOC) and macronutrients [especially phosphorus (P)] and severe soil compaction, which creates an unbalanced ratio between water- and air-filled pore space within the soil and increases mechanical resistance to root growth. Conversion from pasture to sugarcane improved soil chemical quality by correcting for acidity and increasing macronutrient levels. Despite those improvements, most of the P added by fertilizer accumulated in less plant-available P forms, confirming the key role of organic P has in providing available P to plants in Brazilian soils. Long-term sugarcane production subsequently led to further SOC depletions. Sugarcane production had slight negative impacts on soil physical attributes compared to pasture land. Although tillage performed for sugarcane planting and replanting alleviates soil compaction, our data suggested that the effects are short-term with persistent, reoccurring soil consolidation that increases erosion risk over time. These soil physical changes, induced by LUC, were detected by quantitative soil physical properties as well as by visual evaluation of soil structure (VESS), an on-farm and user-friendly method for evaluating SQ. The SMAF efficiently detected overall SQ response to LUC and it could be reliably used under Brazilian soil conditions. Furthermore, since all of the SQI values developed in this study were able to rank SQ among land uses. We recommend that simpler and more cost-effective SQI strategies using a small number of carefully chosen soil indicators, such as: pH, P, K, VESS and SOC, and proportional weighting within of each soil sectors (chemical, physical and biological) be used as a protocol for SQ assessments in Brazilian sugarcane areas. The SMAF and SQI scores suggested that long-term conversion from native vegetation to extensive pasture depleted overall SQ, driven by decreases in chemical, physical and biological indicators. In contrast, conversion from pasture to sugarcane had no negative impacts on overall SQ, mainly because chemical improvements offset negative impacts on biological and physical indicators. Therefore, our findings can be used as scientific base by farmers, extension agents and public policy makers to adopt and develop management strategies that sustain and/or improving SQ and the sustainability of sugarcane production in Brazil.

Real time optimization in chemical process: evaluation of strategies, improvements and industrial application.

The increasing economic competition drives the industry to implement tools that improve their processes efficiencies. The process automation is one of these tools, and the Real Time Optimization (RTO) is an automation methodology that considers economic aspects to update the process control in accordance with market prices and disturbances. Basically, RTO uses a steady-state phenomenological model to predict the process behavior, and then, optimizes an economic objective function subject to this model. Although largely implemented in industry, there is not a general agreement about the benefits of implementing RTO due to some limitations discussed in the present work: structural plant/model mismatch, identifiability issues and low frequency of set points update. Some alternative RTO approaches have been proposed in literature to handle the problem of structural plant/model mismatch. However, there is not a sensible comparison evaluating the scope and limitations of these RTO approaches under different aspects. For this reason, the classical two-step method is compared to more recently derivative-based methods (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality) using a Monte Carlo methodology. The results of this comparison show that the classical RTO method is consistent, providing a model flexible enough to represent the process topology, a parameter estimation method appropriate to handle measurement noise characteristics and a method to improve the sample information quality. At each iteration, the RTO methodology updates some key parameter of the model, where it is possible to observe identifiability issues caused by lack of measurements and measurement noise, resulting in bad prediction ability. Therefore, four different parameter estimation approaches (Rotational Discrimination, Automatic Selection and Parameter estimation, Reparametrization via Differential Geometry and classical nonlinear Least Square) are evaluated with respect to their prediction accuracy, robustness and speed. The results show that the Rotational Discrimination method is the most suitable to be implemented in a RTO framework, since it requires less a priori information, it is simple to be implemented and avoid the overfitting caused by the Least Square method. The third RTO drawback discussed in the present thesis is the low frequency of set points update, this problem increases the period in which the process operates at suboptimum conditions. An alternative to handle this problem is proposed in this thesis, by integrating the classic RTO and Self-Optimizing control (SOC) using a new Model Predictive Control strategy. The new approach demonstrates that it is possible to reduce the problem of low frequency of set points updates, improving the economic performance. Finally, the practical aspects of the RTO implementation are carried out in an industrial case study, a Vapor Recompression Distillation (VRD) process located in Paulínea refinery from Petrobras. The conclusions of this study suggest that the model parameters are successfully estimated by the Rotational Discrimination method; the RTO is able to improve the process profit in about 3%, equivalent to 2 million dollars per year; and the integration of SOC and RTO may be an interesting control alternative for the VRD process.