Design And Synthesis
The creation, evaluation and
optimization of a chemical process is the central task
in process systems engineering. In industry, the goal
has been reduction of the design time as well as the
development of better designs that incorporate life
cycle features of the process. These features include,
of course, a highly profitable and competitive process,
as well as a process that is easy to control and operate.
Moreover, it must be environmentally benign and safe.
The research tasks involved in this task take the following
Typically the bread and butter
of industrial designs, this task deals with the analysis
of a given process. Challenges in process simulation
include the incorporation of more difficult and detailed
process models. These include modeling of process
dynamics as well as steady state, incorporation of
transport models for separation, the simulation of
highly nonideal systems with multiple phases, and
the development of rigorous, first principle reactor
models. These models require simulators to evolve
from modeling and solving algebraic systems of equations
to differential algebraic models and also to consider
PDEs. In addition, the availability and application
of optimization methods has led to a powerful extension
of simulation tools.
This task includes the synthesis
of network structures for heat exchanger networks,
heat pumps and the integration with utility systems.
The impact on the process is a direct reduction in
energy consumption and more efficient energy utilization.
Research in this area deals
with the synthesis of sequences for the separation
of nonideal mixtures, possibly with multiple phases
and highly nonlinear process behavior. Process impacts
include novel approaches and simpler processes for
separation of solvents, byproducts and wastes, with
a direct result on process efficiency and environmental
impact. Another important trend is the design of reactive
distillation systems that can sometimes lead to very
significant cost savings.
Reactors are inherently characterized
by complex nonlinear behavior. Moreover, the process
chemistry has a leading influence on raw material
conversion and on the overall process design. The
synthesis of novel reactor network structures leads
to waste minimizing processes and greater efficiency
in the conversion of raw materials to desired product.
Over the life cycle of the
process, input conditions and product demands change,
feedstock and product specifications may vary and
the process will be subject to short and long term
uncertainties. Moreover, process models are also subject
The challenge therefore is to develop a design that
is tolerant to levels of uncertainty and exhibits a
profitable expected performance. An important case is
also the one of processes under multiperiod operation
that are subjected to a finite number of process variations.
To complement the role of process
design and synthesis, research in process operations
seeks to improve existing operating processes. Through
the development of strategies and analysis tools, improvements
can be found through on-line optimization of a process,
scheduling of operating strategies, changeovers and
interactions between different processes, and overall
planning of product productions to meet market demands.
The research tasks involved in this task take the following
This task is devoted to the
development quantitative measures of process flexibility
as well as strategies that improve these measures
for chemical processes. Here improvements in both
design and operation can be considered to increase
the process' tolerance to uncertainty. This also allows
the process to deal with a wider range of operations
and production scenarios. The formulation of flexibility
problems, either in terms of deterministic or stochastic
measures, leads to large, complex optimization problems
and with challenges for the application on realistic
With short term (e.g., hourly)
changes in feedstock and product demands, the availability
of detailed process models and powerful optimization
tools, it is now possible to optimize steady state
models on-line and to readjust the setpoints of the
control system. This leads to processes that can adapt
to daily fluctuations in inputs and uncertainties.
These can therefore lead too much higher profits.
The current challenge is to deal with dynamic models
in addition to steady state cases and also to provide
a tighter coupling to the process control system.
Scheduling of batch and continuous
processes can have a major impact on the overall profitability
of a process, as well as on the timely delivery of
products. Major problems include sequencing, scheduling
of equipment utilization and maintenance over a planning
horizon, and inventory considerations of a process.
Such problems form perhaps difficult combinatorial
optimization problems but also contribute to high
payoffs. Moreover, the results of this task have a
major impact on the local operation of the process,
and strong interactions exist between the scheduling,
design and operation of the process.
and Supply Chain Management
Production planning and supply
chain management provide the decision support systems
for the logistics in the long range operation of networks
of plants, and their coordination with marketing and
business considerations. These problems give rise
to very large multiperiod optimization problems where
a major challenge lies in the effective aggregation
of more detailed scheduling and operational models.
Process control has evolved into
a strong discipline in process systems engineering.
Traditionally this has been characterized by single
loop PID controllers with incremental advances that
lead to advanced elements in the control system. More
recently, concepts from optimization, mathematical analysis
and nonlinear dynamics have played important roles in
developing more efficient and superior control strategies.
Areas of research can be classified as follows:
- Model Predictive
Developed in the late 70s,
MPC has shown significant advantages over structured
PID control loops and has become the most widely used
multivariable control strategy in industry. This approach
is a generic strategy applied to large classes of
unit operations, but was developed only with linear
process models (usually derived empirically). Only
recently have theoretical properties of these controllers
been developed. Moreover, the discovery of many interesting
properties for control and identification has led
to direct results in tuning and design of these control
systems in industry.
All processes are nonlinear
and in many cases, linear model-based controllers
are no longer satisfactory. To deal with this, geometric
linearization strategies have been developed and lead
to powerful insights in the design of control structures.
Moreover, model predictive control can also be extended
directly to deal with nonlinear dynamic models. Again,
properties relating to the stability, robustness and
performance of these controllers still need to be
explored. Also, industry has had significant successes
with these controllers on batch and semi-continuous
Of Design, Control And Operations
Finally, the ability to develop
large steady state and dynamic process models and to
solve large and complex optimization problems naturally
leads to problem formulations that directly consider
the interactions of process design, control and operations.
The results of this approach lead to powerful synergies
among these tasks, better performance of the process
and improvements in profitability, efficiency and environmental
impact. Challenges related to this approach include
the modeling of quantitative metrics for control, flexibility
and operability and the solution of large optimization
problems with both continuous and discrete decision
Tools And Methods For Process Systems Engineering
All the above areas in process
systems engineering are supported by a large number
of tools and algorithms which are the subject of active
research efforts. These include modeling systems for
simulation, optimization and control; algorithms for
algebraic/differential equations and integral/PDE systems;
algorithms for linear and nonlinear discrete and continuous
optimization; algorithms for global and stochastic optimization;
information management systems and data bases; advanced
computer architectures for parallel computation.