21,887 research outputs found
Optimal Design and Operation of Heat Exchanger Network
Heat exchanger networks (HENs) are the backbone of heat integration due to
their ability in energy and environmental managements. This thesis deals with
two issues on HENs. The first concerns with designing of economically optimal
Heat exchanger network (HEN) whereas the second focus on optimal operation
of HEN in the presence of uncertainties and disturbances within the network. In
the first issue, a pinch technology based optimal HEN design is firstly
implemented on a 3–streams heat recovery case study to design a simple HEN
and then, a more complex HEN is designed for a coal-fired power plant retrofitted
with CO2 capture unit to achieve the objectives of minimising energy penalty on
the power plant due to its integration with the CO2 capture plant. The benchmark
in this case study is a stream data from (Khalilpour and Abbas, 2011).
Improvement to their work includes: (1) the use of economic data to evaluate
achievable trade-offs between energy, capital and utility cost for determination of
minimum temperature difference; (2) redesigning of the HEN based on the new
minimum temperature difference and (3) its comparison with the base case
design. The results shows that the energy burden imposed on the power plant
with CO2 capture is significantly reduced through HEN leading to utility cost
saving maximisation. The cost of addition of HEN is recoverable within a short
payback period of about 2.8 years. In the second issue, optimal HEN operation
considering range of uncertainties and disturbances in flowrates and inlet stream
temperatures while minimizing utility consumption at constant target
temperatures based on self-optimizing control (SOC) strategy. The new SOC
method developed in this thesis is a data-driven SOC method which uses process
data collected overtime during plant operation to select control variables (CVs).
This is in contrast to the existing SOC strategies in which the CV selection
requires process model to be linearized for nonlinear processes which leads to
unaccounted losses due to linearization errors. The new approach selects CVs
in which the necessary condition of optimality (NCO) is directly approximated by
the CV through a single regression step. This work was inspired by Ye et al.,
(2013) regression based globally optimal CV selection with no model linearization
and Ye et al., (2012) two steps regression based data-driven CV selection but with poor optimal results due to regression errors in the two steps procedures.
The advantage of this work is that it doesn’t require evaluation of derivatives
hence CVs can be evaluated even with commercial simulators such as HYSYS
and UNISIM from among others. The effectiveness of the proposed method is
again applied to the 3-streams HEN case study and also the HEN for coal-fired
power plant with CO2 capture unit. The case studies show that the proposed
methodology provides better optimal operation under uncertainties when
compared to the existing model-based SOC techniques
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
Constrained optimization of high-dimensional numerical problems plays an
important role in many scientific and industrial applications. Function
evaluations in many industrial applications are severely limited and no
analytical information about objective function and constraint functions is
available. For such expensive black-box optimization tasks, the constraint
optimization algorithm COBRA was proposed, making use of RBF surrogate modeling
for both the objective and the constraint functions. COBRA has shown remarkable
success in solving reliably complex benchmark problems in less than 500
function evaluations. Unfortunately, COBRA requires careful adjustment of
parameters in order to do so.
In this work we present a new self-adjusting algorithm SACOBRA, which is
based on COBRA and capable to achieve high-quality results with very few
function evaluations and no parameter tuning. It is shown with the help of
performance profiles on a set of benchmark problems (G-problems, MOPTA08) that
SACOBRA consistently outperforms any COBRA algorithm with fixed parameter
setting. We analyze the importance of the several new elements in SACOBRA and
find that each element of SACOBRA plays a role to boost up the overall
optimization performance. We discuss the reasons behind and get in this way a
better understanding of high-quality RBF surrogate modeling
A goal programming methodology for multiobjective optimization of distributed energy hubs operation
This paper addresses the problem of optimal energy flow management in multicarrier energy networks
in the presence of interconnected energy hubs. The overall problem is here formalized by a nonlinear
constrained multiobjective optimization problem and solved by a goal attainment based methodology.
The application of this solution approach allows the analyst to identify the optimal operation state of the
distributed energy hubs which ensures an effective and reliable operation of the multicarrier energy
network in spite of large variations of load demands and energy prices. Simulation results obtained on
the 30 bus IEEE test network are presented and discussed in order to demonstrate the significance and
the validity of the proposed method
Optimal Networks from Error Correcting Codes
To address growth challenges facing large Data Centers and supercomputing
clusters a new construction is presented for scalable, high throughput, low
latency networks. The resulting networks require 1.5-5 times fewer switches,
2-6 times fewer cables, have 1.2-2 times lower latency and correspondingly
lower congestion and packet losses than the best present or proposed networks
providing the same number of ports at the same total bisection. These advantage
ratios increase with network size. The key new ingredient is the exact
equivalence discovered between the problem of maximizing network bisection for
large classes of practically interesting Cayley graphs and the problem of
maximizing codeword distance for linear error correcting codes. Resulting
translation recipe converts existent optimal error correcting codes into
optimal throughput networks.Comment: 14 pages, accepted at ANCS 2013 conferenc
Data-Driven Robust Optimization
The last decade witnessed an explosion in the availability of data for
operations research applications. Motivated by this growing availability, we
propose a novel schema for utilizing data to design uncertainty sets for robust
optimization using statistical hypothesis tests. The approach is flexible and
widely applicable, and robust optimization problems built from our new sets are
computationally tractable, both theoretically and practically. Furthermore,
optimal solutions to these problems enjoy a strong, finite-sample probabilistic
guarantee. \edit{We describe concrete procedures for choosing an appropriate
set for a given application and applying our approach to multiple uncertain
constraints. Computational evidence in portfolio management and queuing confirm
that our data-driven sets significantly outperform traditional robust
optimization techniques whenever data is available.Comment: 38 pages, 15 page appendix, 7 figures. This version updated as of
Oct. 201
Optimizing I/O for Big Array Analytics
Big array analytics is becoming indispensable in answering important
scientific and business questions. Most analysis tasks consist of multiple
steps, each making one or multiple passes over the arrays to be analyzed and
generating intermediate results. In the big data setting, I/O optimization is a
key to efficient analytics. In this paper, we develop a framework and
techniques for capturing a broad range of analysis tasks expressible in
nested-loop forms, representing them in a declarative way, and optimizing their
I/O by identifying sharing opportunities. Experiment results show that our
optimizer is capable of finding execution plans that exploit nontrivial I/O
sharing opportunities with significant savings.Comment: VLDB201
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