<p> This thesis considers the problem of control of nonlinear process systems subject to input constraints and faults in the control actuators and process equipments. Faults are considered that preclude the possibility of continued operating at the nominal equilibrium point and a framework (which we call the safe-parking framework) is developed to enable efficient resumption of nominal operation upon fault-recovery. First, Lyapunov-based model predictive controllers, that allow for an explicit characterization of the stability region subject to constraints on the manipulated input, are designed. The stability region characterization is utilized in selecting 'safe-park' points from the safe-park candidates (equilibrium points subject to failed actuators). This safe-park point is chosen as a temporary operating point where process is to be operated during fault rectification. This ensures that process can be safely operated during fault rectification and the nominal operation can be resumed upon fault recovery. When multiple candidate safe-park points are available, performance considerations, such as ease of transition from and to the safe-park point and cost of running the process at the safe-park point, are quantified and utilized in choosing the optimal safe-park point. </p> <p> Next, we extend the safe-parking framework to handle practical issues such as plant-model mismatch, disturbances and unavailability of all process state measurements. \i\Te first consider the presence of constraints and uncertainty and develop a robust Lyapunov-based model predictive controller. This controller is utilized to characterize robust stability region which, subsequently, is utilized to select 'safepark' points. Then we consider the problem of availability of limited measurements. An output feedback Lyapunov-based model predictive controller, with high-gain observer to estimate unmeasured states, is formulated and its stability region explicitly characterized. An algorithm is then presented that accounts for the estimation errors in the implementation of the safe-parking framework. </p> <p> We then further extend the framework to handle faults in large scale chemical plants where multiple process units are connected via material, energy and information streams. In plant-wide setting, the safe-park point for the faulty unit is chosen such that the safe-parking has no or minimum effect on downstream units, and hence, the nominal operation in the downstream units can be continued. Next we consider the scenario where no viable safe-park point for the faulty unit exists such that its effect can be completely absorbed in the subsequent unit. A methodology is developed that allows simultaneous safe-parking of the consecutive units. The efficacy of the proposed framework is illustrated using a chemical reactor example, a styrene polymerization process and two CSTRs in series example. </p> <p> Finally, we demonstrate the efficacy of proposed Lyapunov based Model Predictive Controller and Safe-Parking framework on a polymerization reactor model to control the polymerization reactor and to handle faults that dont allow continuation of the nominal operation in the reactor. </p> / Thesis / Doctor of Philosophy (PhD)

Pancreatic cancer (PC) is a challenging cancer to treat, with a 5 year survival of < 13% in the United States. This is attributed to multiple histologic subtypes, extensive desmoplastic reactions, resistance to chemotherapy, profound immunosuppression and crosstalk between tumor, immune and stromal cells in the microenvironment. Alternative modalities are needed to treat this aggressive tumor. Our laboratory has shown that targeting polyamines using a polyamine blockade therapy (PBT), which is a combination of Difluoromethylornithine (polyamine synthesis inhibitor) and Trimer44NMe (polyamine transport inhibitor), is effective against PC. In the present study, we used a KPC genetic model of PC, as it mimics human tumors from spontaneous tumor conception to metastasis. Despite tumor heterogeneity, PBT improved outcomes in KPC mice. Histopathology revealed decreased tumor size, variable decrease in tumor weights, and significant stromal alterations. Stromal alterations were driven by reduced collagen deposition. PBT was found to variably inhibit markers associated with cancer-associated fibroblasts and activated pancreatic stellate cells, which are key producers of collagen. Also, M1 macrophage associated markers were upregulated in the microenvironment. To elucidate the effect of PBT on cells known to contribute to the immunosuppressive environment in PC, we treated bone marrow-derived myeloid derived suppressor cells (MDSC). We found using flow cytometry that PBT inhibited the abundance of PMN (polymorphonuclear)-MDSC phenotype. Finally, RNA sequencing revealed that PBT inhibited genes involved in chemotaxis and inflammation associated with MDSC biology. Collectively, this work provides the basis for feasibility and utility of testing PBT in larger cohorts of the KPC model.

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