rama R Karri

Singapore Strait located between the South China Sea and Andaman Sea is driven by tides coming from both sides and the hydrodynamics in this area is complex. From the viewpoint of long term forecasting, however, models developed for this area suffer from limitations introduced by parametric uncertainty, absence of data for appropriate specification of forcing and lateral boundary conditions. For improving the model forecasts, a data assimilation technique based on ensemble Kalman filter is implemented and applied. Based on the latter, an ensemble based steady state Kalman filter is derived to address the computational limitation for daily operational forecasting. Via a twin experiment on a simulation period that includes a significant storm surge event (sea level anomaly) the skills of both data assimilation schemes are assessed and compared.

Control of nonlinear systems exhibiting complex dynamic behavior is a challenging task because such systems present a variety of behavioral patterns depending on the values of physical parameters and intrinsic features. Understanding the behavior of the nonlinear dynamic systems and controlling them at the desired conditions is important to enhance their performance. In this work, a soft sensor based nonlinear controller strategy is presented and applied to control a chemical reactor that exhibit multi-stationary unstable behavior, oscillations and chaos. In this strategy, an extended kalman filter is designed to serve as a soft sensor that provides the estimates of unmeasured process states. These states are used as inferential measurements to the nonlinear controller that is designed in the framework of globally linearizing control. The results evaluated for stabilizing the reactor for different conditions including deterministic and stochastic disturbances show the better performance of the soft sensor based nonlinear control strategy over that of a PID controller with modified feedback mechanism.

Successful operation and control of complex dynamic systems heavily rely on
the availability of fast and accurate evaluation of the system performance. The
measurement problems and the delays associated with these systems require the
need for on-line state estimators as alternative measurement tools. In this work,
a state estimation method based on extended Kalman filter (EKF) is presented
for nonlinear dynamical systems that are characterized by complex dynamic phenomena such as multiple steady state behavior, limit cycle oscillations and chaos.
The estimator uses the mathematical model of the process in conjunction with
the known process measurements to provide the unmeasured process states that
capture the fast changing nonlinear dynamics of the process. The design and performance of the state estimator is evaluated by applying two typical continuous
non-isothermal nonlinear processes, a chemical reactor and a polymerization reactor, which show rich dynamical behavior ranging from stable situations to chaos.
In order to understand the dynamic phenomena and to analyze the conditions that
lead to an improved operation, prior to state estimation, these processes are thoroughly analyzed for multiplicity, stability and bifurcation studies. The sensitivity
of the state estimator is also studied towards the effect of the design parameters
involved in the method. The results demonstrate the efficacy of the model based
method for state estimation in nonlinear chemical processes associated with complex dynamic behavior.

Inverse estimation of model parameters via mathematical modeling route, known as inverse modeling (IM), is an attractive alternative approach to the experimental methods. This approach makes use of efficient optimization techniques in the course of solution of an inverse problem with the aid of measured data. In this study, a novel optimization method based on ant colony optimization (ACO), denoted by ACO-IM, is presented for inverse estimation of kinetic and film thickness parameters of biofilm models that describe an experimental fixed bed anaerobic reactor. The proposed optimization method for parameter estimation emulates the fact that ants are capable of finding the shortest path from a food source to their nest by depositing a trial of pheromone during their walk. The efficacy of the ACO-IM for numerical estimation of bio-kinetic parameters is demonstrated through its application for the anaerobic treatment of industry wastewater in a fixed bed biofilm process. The results explain the rigorousness of
mathematical models, the form of kinetic and film thickness models and the type of packing to be used with the biofilm process for accurate determination of kinetic and film thickness parameters so as to ensure reliable predictive performance of the biofilm reactor models.