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.

Primary and secondary metabolites production in signal grass around the year under nitrogen fertilizer

Plants produce a number of substances and products and primary and secondary metabolites (SM) are amongst them with many benefits but limitation as well. Usually, the fodder are not considered toxic to animals or as a source having higher SM. The Brachiaria decumbens has a considerable nutritional value, but it is considered as a toxic grass for causing photosensitization in animals, if the grass is not harvested for more than 30 days or solely. The absence of detailed information in the literature about SM in Brachiaria, metabolites production and its chemical profile enable us to focus not only on the nutritive value but to get answers in all aspects and especially on toxicity. The study was conducted in the period of december 2013 to december 2014; in greenhouse FZEA-USP. B. decumbens was used with two cutting heights (10 and 20 cm) and nitrogen doses (0, 150, 300 and 450 kg ha-1) in complete randomized block design. The bromatological analysis were carried out on near infrared spectroscopy. Generally, the application of 150 kg ha-1 N was sufficient to promote the nutritional value in B. decumbens but above it the nitrogen use efficiency decline significantly. The highest dry matter yield (99.97 g/pot) was observed in autumn and the lowest was in winter (30.20 g/pot). While, as per nitrogen dose the average highest dry matter yield was at 150 kg ha-1 (79.98 g/pot). The highest crude protein was observed in winter (11.88%) and the lowest in autumn (7.78%). By the cutting heights; the 10 cm proved to have high CP (9.51%). In respect of fibrous contents, the highest acid detergent fiber was noted in summer (36.37%) and lowest in winter (30.88%). While the neutral detergent fiber was being highest in autumn and lowest in spring (79.60%). The highest in vitro dry matter and organic matter digestibilities were noted at 300 kg ha-1 N; being 68.06 and 60.57%; respectively; with the lowest observed in without N treatments (62.63% and 57.97), respectively. For determination of the classes, types and concentration of SM in B. decumbens, phytochemical tests, thin layer and liquid chromatography-mass spectrometry and nuclear magnetic resonance analysis were carried out. Height, nitrogen and seasons significantly (P <0.0001) affected the secondary metabolic profile. A new protodioscin isomer (protoneodioscin (25S-)) was identified for first time in B. decumbens and is supposed to be the probable toxicity reason. Its structure was verified by 1D and 2D NMR techniques (1H, 13C) and 1D (COSY-45, edited HSQC, HMBC, H2BC, HSQC -TOCSY, NOESY and 1 H, 1 H, J). All factors influence the metabolic profile significantly (P <0.0001). The lowest phenols were at 300 kg ha-1 while the lowest flavones were at 0 kg ha-1. Season wise the highest phenols occurred in autumn (19.65 mg/g d.wt.) and highest flavones (28.87 mg/g d.wt.) in spring. Seasons effect the saponin production significantly (P <0.0001) and the results showed significant differences in the protodioscin (17.63±4.3 - 22.57±2.2 mg/g d.wt.) and protoneodioscin (23.3±1.2 - 31.07±2.9 mg/g d.wt.) concentrations. The highest protodioscin isomers concentrations were observed in winter and spring and by N doses the highest were noted in 300 kg ha-1. Simply, all factors significantly played their role in varying concentrations of secondary metabolites.