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An Algorithm For Supervisory Multivariable Constrained Optimization Of Industrial Processes., William E. Daniel Jr
An Algorithm For Supervisory Multivariable Constrained Optimization Of Industrial Processes., William E. Daniel Jr
LSU Historical Dissertations and Theses
An algorithm is presented for supervisory optimization of industrial processes that combines the minimization of operating costs with process operating constraints. The supervisory algorithm manipulates the set points of a lower-level control system and the set points are updated at long enough intervals of time so that the process reaches steady state between set point updates. This steady state assumption greatly simplifies the algorithm computations and, more importantly, significantly reduces the effort required for process identification. This dissertation develops the algorithm and then presents results from its application to a simulated distillation train and reactor feed network. In both applications, …
Mathematical Model Of Hairy Root Growth., Seungdo Kim
Mathematical Model Of Hairy Root Growth., Seungdo Kim
LSU Historical Dissertations and Theses
Hairy roots produce secondary metabolites which have been used as specialty chemicals. The mathematical models of hairy root growth kinetics can guide the design and scale-up of root cultures. An unstructured segregated and a chemically structured segregated models for hairy growth kinetics are formulated with rules of root branching processes. The deterministic branching rule and the stochastic branching rule are studied. For the deterministic rule where two branching parameters are assumed constant, the number of tips can be obtained by solving a linear difference equation. The conceptual framework established for the deterministic rule is used to formulate a stochastic rule …