| Our research is
concerned with
the development of computer aided techniques to improve process design
and operations. More specifically, issues related to developing and
analyzing
efficient models and solution methodologies for product and
process
design and process operations are addressed.
Short-term Scheduling in a Dynamic
Environment
The main goal of this project is to develop an integrated methodology
to handle uncertainty in short-term scheduling problem. Recognizing the
fact that real plants exist in a dynamic environment where the
scheduling
parameters change as the schedule is being executed, a schedule
developed
before hand may become inefficient or even infeasible. The ability to
react
to unexpected events during the execution of a schedule is an important
part of plant operating strategy. The uncertainty considered in this
work
can be viewed as (a) fluctuations in product demands, prices and
processing
times, and (b) unexpected deviations in unit availability (machine
break-down).
First, the expected range of parameter uncertainty is incorporated
within
the short-term scheduling model so as to determine a robust schedule
capable
of meeting the expected range of uncertain parameter values. This is
achieved
by exploiting and further analyzing the sensitivity information
provided
by the solution of the original Mixed Integer Linear Programming (MILP)
problem. The second objective is to develop efficient models that
systematically
incorporate different alternatives concerning the rescheduling
decisions.
The goal is to determine the optimal rescheduling policy that minimizes
the deviations from the old schedule while taking into account the
satisfaction
of other production decisions.
Design of Non-continuous
Multi-product Chemical
Plants: Optimization of Long-Term Plant Performance
The manufacturing of bulk drugs, intermediates and fine chemicals at
large is characterized by a high level of uncertainty regarding the
product
mix (which products, when and at what volumes) over the intermediate
and
long-terms. These uncertainties also apply to the makers of the fine
chemical
intermediates who must ramp up their capacities even sooner sometimes
before
the target drug can be projected to be clinically useful or safe. The
objective
of this project is to develop a systematic methodology for the design
of
very agile, large-scale pilot plants capable of consecutively running
any
of a large number of unplanned chemical steps without significant
modifications.
It is proposed that systematic process design methods can be developed
to impact the key pilot plant attributes to a large capacity commercial
plant for the production of fine chemicals/intermediate and bulk drugs.
This target will be reached without excessive capital outlays,
resulting
in a manufacturing capability that is agile, productive and sparing of
capital investment over its useful life. Specifically, it is proposed
to
bring together a large assembly of bulk drug processing skills and
advanced
mathematical methods that have shown success in process design under
uncertainty.
Modeling of Reactive Flow Processes
Reactor models based on first principles have been proved to provide
accurate results over a wide range of operating conditions, reactor
types,
charges and transport dynamics. Unfortunately though a complex kinetic
network is most commonly needed that results in excessive computational
expense even with the present increased computer power.
The challenging problem lies on the interaction of the various physical
processes with complex reaction kinetics in turbulent reacting flows.
The
main limitation is the existence of a wide spectrum of time and length
scales in turbulent flows that makes Direct Numerical Simulation (DNS)
of Navier-Stokes equations infeasible in terms of computational
requirements.
Thus simplified mixing and reaction models are needed as tools for the
description of complex reaction systems.
In this work we are proposing first to decouple these two important
issues by developing:
(a) an efficient approach for reduction of kinetic models, and
(b) a sufficiently detailed mixing model and
then investigate ways of taking into account mixing effects at the
stage of reduction.
Moreover, uncertainty considerations are of great interest in this
project. In kinetic modeling some of the potential sources of
uncertainty
include reaction rate parameters, thermodynamic parameters, such as
species
heat of formation and entropy, initial conditions and transport
properties.
The objective of this work is to investigate the effects of uncertainty
in complex reaction networks. More specifically the questions that are
addressed are the following. First, what is the range of validity where
the kinetic model is valid, second, how uncertainty information can be
incorporated in the mechanism generation so as to determine a kinetic
model
feasible for the whole range of uncertainty and finally how uncertainty
propagates through the system. Particular emphasis is given to
environmental
and combustion systems.
Domain Decomposition Approach for
Complex Multi-scale
Dynamic Systems
A major challenge in modeling macroscopic chemical and separation
systems
is that elementary processes encompass a wide range of length and time
scales. The disparity of scales necessitates the use of different
approaches
at each scale for solving such problems. Simultaneous consideration
within
a single uniform framework requires excessive computing times and
moreover
there might exist more exact solution techniques for addressing each
problem
separately. Our work focuses on the development of large-scale
computational
methods that allow the analysis of chemical and separation processes
with
appropriate spatial and temporal resolution at multiple length and time
scales. Special attention is devoted to the coupling with other
physical
processes and the inherent multi-dimensionality of the problems. The
computational
methods developed in our research are novel domain decomposition
techniques
based on modified block-waveform relaxation coupled with
subspace-iteration
methods.
Issues of Uncertainty in a Supply
Chain Management
This project focuses first on the development of new ways to quantify
the performance of the system under uncertainty, and techniques of
uncertainty
propagation, and second the application of such uncertainty analysis
tools
within the plant operations. This encompasses scheduling, planning and
supply chain management for which modeling efforts are being currently
developed in my group.
Existing approaches for uncertainty analysis either are two expensive
computationally (e.g. Monte Carlo techniques) or are too limited in
terms
of the amount of information that one can obtain (e.g. flexibility
analysis).
A well-recognized technique that targets on a similar objective is the
Six Sigma analysis tool. What this tool achieves is to measure not only
the nominal process performance but also its whole distribution that
can
be quantified by its variance (sigma). It is well known that moving
towards
the tail of the distribution the probability of fault behavior is
getting
smaller, thus process that are characterized by a large range of
distribution
have very small probability for "bad" performance and consequently they
are much more profitable.
This work targets the development of efficient and systematic
techniques to quantify the range of variability of systems given
uncertainty
in process parameters. A comprehensive approach will take into account
specific system's behavior and model predictive techniques so as to
identify
a critical set of required experiments. The work will be extending on
developing
efficient propagation techniques for complex dynamic systems.
Stochastic
Surface Response methods will be used as an alternative to traditional
Monte Carlo to reduce the enormous computational complexity. Extensions
of these techniques to include time and spatial variability explicitly
will be also investigated.
|
Recent Publications
Goyal, V. and M.G. Ierapetritou.
Framework for Evaluating the
Feasibility/Operability of Noncovex Processes.
AIChE J., 49, 1233, 2003.
Banerjee, I., and M.G. Ierapetritou.
Development of an
Adaptive Chemistry Model Considering Micromixing Effects.
Chem. Eng. Sci, 58,
4537, 2003.
Wu, D., and M.G. Ierapetritou. Decomposition
Approaches for the Efficient Solution of Short-Term
Scheduling Problem.
Comp. Chem. Eng., 27,
1261, 2003.
Jia, Z., and M.G. Ierapetritou.
Mixed Integer Linear
Programming Model for Gasoline Blending and Distribution Scheduling.
Ind. Eng. & Chem. Res., 42,
825, 2003.
Bindal, A., M.G. Ierapetritou
and J.Khinast.
Adaptive Multiscale Solution
Of Dynamical
Systems In Chemical Processes Using Wavelets.
Comp. Chem. Eng.,
27, 131, 2003.
Banerjee, I., and M.G. Ierapetritou.
Process Synthesis under
Parameter Variability.
Comput. Chem. Eng., 27, 1499,2003.
Goyal, V. and M.G. Ierapetritou.
Integration of Data Analysis
and Design Optimization for the systematic Generation of Equipment
Portfolio.
Ind. Eng. & Chem. Res.,
42, 5204, 2003.
Jia, Z., M.G. Ierapetritou and
J. D. Kelly.
Refinery Short-term
Scheduling Using Continuous Time Formulation Crude Oil Operations.
Ind. Eng. & Chem. Res., 42, 3085, 2003.
|
