%0 PDF
%T Data-Driven Dynamic Models for
Nonlinear Process Optimization and
Control
%A Wang, Zhenyu.
%D 2018-03-16T09:33:52.457-04:00
%8 2018-03-16
%R http://localhost/files/3484zv59f
%X Abstract:
Mathematical models play an essential role for the purposes of process optimization and
control. There are two major information sources for the development of these models:
the knowledge of the process inner workings and the input-output data set. The model
estimated using the detailed knowledge are called the knowledge-driven model. However,
the inner workings of many industrial processes are not always fully understood to
enable the development of accurate knowledge-driven models. In such a situation, the
data-driven model, relying on the input-output data, is an attractive alternative. Among
varieties of data-driven modeling approaches, the Design of Dynamic Experiments (DoDE),
a generalization of the traditional Design of Experiments (DoE) approach, has been
demonstrated as an effective modeling methodology for optimizing nonlinear processes.
When time-resolved data are obtainable during the experiments, developing a Dynamic
Response Surface Methodology (DRSM) model is more favorable. As the estimated DRSM model
with time-varying parameters captures the process dynamics, it has the potential to be
applied for not only the process optimization but also the process control purposes. The
main goal of this research work is to further advance and improve the two data-driven
methodologies, the DoDE and the DRSM, to model, optimize and control nonlinear
processes. We first proposed ways to incorporate prior process knowledge to improve the
design of the input domain, in which the time-varying input of the DoDE experiments are
selected. Improved process performance has been achieved in the refined input domain. In
addition, as process optimization is usually under budgetary and time constraint, we
developed an evolutionary DoDE approach to optimize the processes in a timely manner.
The size of the initial set of experiments has been dramatically reduced while the
achieved optimal process performance is similar to the one obtained using the original
DoDE approach. To extend the applicability of the original DRSM approach (DRSM-1) to
deal with processes with various and infinite time duration, we proposed a new DRSM
approach (DRSM-2). The novelty of the DRSM-2 rests on a nonlinear transformation of
time, the independent variable. Comparing to the DRSM-1, the new method has the
following advantages. It is capable of 1) Modeling both continuous as well as batch
processes, handling semi-infinite as easily as finite time domains 2) Using data that
are not equidistant in time 3) Using data segments that are of varied durations due to
possible strong nonlinearities in dynamics We also developed a single model approach,
using the DRSM model, for both process optimization and control purposes. The proposed
method reduces the experimental effort comparing to the current practices which use
separate models for process optimization and control purposes, respectively. When the
number of measurements is small, the proposed approach provides better control
performance compared to the performance achieved using a model estimated with Pseudo
Random Binary Signal (PRBS) data.; Thesis
(Ph.D.)--Tufts University, 2018.; Submitted to the
Dept. of Chemical and Biological
Engineering.; Advisor: Christos
Georgakis.; Committee: Eric Miller, David Schmidt,
and Emmanuel Tzanakakis.; Keyword: Chemical
engineering.
%[ 2022-10-11
%9 Text
%~ Tufts Digital Library
%W Institution