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.

Pós-tratamento físico-químico por flotação de efluentes de reatores anaeróbios de manta de lodo (UASB)

O emprego da flotação por ar dissolvido (FAD) para o pós-tratamento de efluentes de reatores anaeróbios aparenta ser atraente considerando algumas características desse processo físico-químico. A FAD é reconhecidamente um processo de alta taxa, particularmente eficiente na remoção de material particulado em suspensão e de flocos produzidos pela coagulação química de águas residuárias. Além disso, há produção de lodo espesso e provavelmente arraste de parcela de gases e de compostos voláteis, presentes nos efluentes anaeróbios. Entretanto, a concepção de sistemas de FAD deve ser precedida por ensaios em unidades de flotação em escala de laboratório, permitindo a determinação dos principais parâmetros do processo. Neste trabalho, são apresentados e discutidos os resultados obtidos em laboratório e em instalação piloto de flotação com escoamento contínuo recebendo efluente de reator anaeróbio de manta de lodo (UASB), com 18 m3 de volume, tratando esgoto sanitário. Os ensaios em unidade em escala de laboratório foram realizados utilizando diferentes dosagens de cloreto férrico (entre 30 e 110 mg/L) ou de polímero catiônico (entre 1,0 e 16,0 mg/L), atuando como coagulantes. Além disso, foram estudadas as condições de floculação (tempo de 15 e de 25 min, e gradiente médio de velocidade de floculação entre 30 e 100 s-1) e diferentes valores de quantidade de ar fornecido ao processo (S*, entre 4,7 e 28,5 g de ar por m3 de efluente). Com a instalação piloto de FAD foram realizados apenas ensaios preliminares variando-se a taxa de aplicação superficial (140 e 210 m3/m2/d) para diferentes valores de S* (14,8 a 29,5 g de ar por m3 de efluente). Com o emprego de dosagem de 65 mg/L de cloreto férrico, de tempo de 15 min e gradiente médio de velocidade de floculação de 80 s-1 e de 19 g de ar por m3 de efluente, foram observados excelentes resultados em laboratório, com elevadas remoções de DQO (89%), de fosfato total (96%), de sólidos suspensos totais (96%), de turbidez (98%), de cor aparente (91%), de sulfetos (não detectado) e NTK (47%). Considerando o sistema UASB e FAD, nos testes em laboratório, foram observadas remoções globais de 97,7% de DQO, de 98,0% de fosfato total, de 98,9% de SST, de 99,5% de turbidez, de 97,8% de cor aparente e de 59,0% de NTK. Nos ensaios com a instalação piloto de FAD, o sistema apresentou remoções de 93,6% de DQO, de 87,1% de SST, de 90% de sulfetos e de 30% de NTK.

Infrared microspectroscopy of inflammatory process and colon tumors.

According to the last global burden of disease published by the World Health Organization, tumors were the third leading cause of death worldwide in 2004. Among the different types of tumors, colorectal cancer ranks as the fourth most lethal. To date, tumor diagnosis is based mainly on the identification of morphological changes in tissues. Considering that these changes appears after many biochemical reactions, the development of vibrational techniques may contribute to the early detection of tumors, since they are able to detect such reactions. The present study aimed to develop a methodology based on infrared microspectroscopy to characterize colon samples, providing complementary information to the pathologist and facilitating the early diagnosis of tumors. The study groups were composed by human colon samples obtained from paraffin-embedded biopsies. The groups are divided in normal (n=20), inflammation (n=17) and tumor (n=18). Two adjacent slices were acquired from each block. The first one was subjected to chemical dewaxing and H&E staining. The infrared imaging was performed on the second slice, which was not dewaxed or stained. A computational preprocessing methodology was employed to identify the paraffin in the images and to perform spectral baseline correction. Such methodology was adapted to include two types of spectral quality control. Afterwards the preprocessing step, spectra belonging to the same image were analyzed and grouped according to their biochemical similarities. One pathologist associated each obtained group with some histological structure based on the H&E stained slice. Such analysis highlighted the biochemical differences between the three studied groups. Results showed that severe inflammation presents biochemical features similar to the tumors ones, indicating that tumors can develop from inflammatory process. A spectral database was constructed containing the biochemical information identified in the previous step. Spectra obtained from new samples were confronted with the database information, leading to their classification into one of the three groups: normal, inflammation or tumor. Internal and external validation were performed based on the classification sensitivity, specificity and accuracy. Comparison between the classification results and H&E stained sections revealed some discrepancies. Some regions histologically normal were identified as inflammation by the classification algorithm. Similarly, some regions presenting inflammatory lesions in the stained section were classified into the tumor group. Such differences were considered as misclassification, but they may actually evidence that biochemical changes are in course in the analyzed sample. In the latter case, the method developed throughout this thesis would have proved able to identify early stages of inflammatory and tumor lesions. It is necessary to perform additional experiments to elucidate this discrepancy between the classification results and the morphological features. One solution would be the use of immunohistochemistry techniques with specific markers for tumor and inflammation. Another option includes the recovering of the medical records of patients who participated in this study in order to check, in later times to the biopsy collection, whether they actually developed the lesions supposedly detected in this research.