Hard combinatorial optimization (CO) problems pose challenges to traditional algorithmic solutions. The search space usually contains a large number of local optimal points and the computational cost to reach a global optimum may be too high for practical use. In this work, we conduct a comparative study of several state-of-the-art metaheuristic algorithms for hard CO problems solving. Our study is motivated by an industrial application called the Fertilizer Blends Optimization. We focus our study on a number of local search metaheuristics and analyze their performance in terms of both runtime efficiency and solution quality. We show that local search granularity (move step size) and the downhill move probability are two major factors that affect algorithm performance, and we demonstrate how experimental tuning work can be applied to obtain good performance of the algorithms. <p>Our empirical result suggests that the well-known Simulated Annealing (SA) algorithm showed the best performance on the fertilizer problem. The simple Iterated Improvement Algorithm (IIA) also performed surprisingly well by combining strict uphill move and random neighborhood selection. A novel approach, called Delivery Network Model (DNM) algorithm, was also shown to be competitive, but it has the disadvantage of being very sensitive to local search granularity. The constructive local search method (GRASP), which combines heuristic space sampling and local search, outperformed IIA without a construction phase; however, the improvement in performance is limited and generally speaking, local search performance is not sensitive to initial search positions in our studied fertilizer problem.

The main purpose of this study is to investigate the relationship between the water turbidity purification result with raw water turbidity, raw water pH value and PAC dosage, and find the optimal treatment dosage level. A regression model between the response and treatment variables has been built with a two-stage procedure. In the first stage, the regression model treats a given raw water turbidity as the explanatory variable and the best treatment effect dosage level, among the six experimental dosage levels as the response. Later the model is used to find the second stage regression model where the water turbidity purification result is treated as the response, and the other three variables mentioned above as explanatory variables. According to the results of the second stage regression model about the best dosage level with a given raw water turbidity, the optimal PAC dosage is estimated for the optimization in water turbidity purification, which may be used as a way for future purification of water turbidity.

This Record of Study contains the details of an optimization model developed for Shell Oil Co. This model will be used during the strategic design process of a supply chain for a new technology commercialization. Unlike traditional supply chain deterministic optimization, this model incorporates different levels of uncertainty at suppliers’ nominal capacity. Because of the presence of uncertainty at the supply stage, the objective of this model is to define the best diversification and safety stock level allocated to each supplier, which minimize the total expected supply chain cost. We propose a Monte Carlo approach for scenario generation, a two-stage non-linear formulation and the Sample Average Approximation (SAA) procedure to solve the problem near optimality. We also propose a simple heuristic procedure to avoid the nonlinearity issue. The sampling and heuristic optimization procedures were implemented in a spreadsheet with a user’s interface. The main result of this development is the analysis of the impact of diversification in strategic sourcing decisions, in the presence of stochastic supply disruptions.

Natural gas and petroleum are non-renewable and scarce energy sources. Although, it is well known that hydrocarbon reserves are depleting through the years, oil and gas remain the principal source of energy upon which our society is strongly dependent. Hence, optimization and accurate planning of hydrocarbon production are the main keys to making it safer, more efficient, and cheaper. One of the tools commonly used to evaluate the optimization of oil/gas production system is the process simulation modeling. A hydrocarbon production system typically consists of at least one underground reservoir where several wells have been drilled into the hydrocarbon-bearing rock to form a fixed topology network. Wells are interconnected with manifolds to transport the gas or oil to a storage or sale location. The process simulation consists of calculating the total hydrocarbon production for the given production system. The pressure in the wellbore is the main variable in determining the hydrocarbon production process. When oil/gas is produced, the pressure decreases until production cannot be sustained. If the well is shut down, the pressure at the wellbore increases because of the natural gas flow coming from the reservoir. In addition, artificial lift techniques, such as water injection, gas lift and pump systems can be incorporated into the simulation program. The oil/gas production has been also modeled as a multi-period optimization case to incorporate the possibility of different demands, cost and overall time behavior. The current field optimization approaches take in account the availability in a general way, adding to the planning a lot of uncertainty. The proposed study includes a suitable analysis of the likelihood of equipment failure, which will predict the availability of the equipment in a certain period of time to perform a more accurate planning. In this work, we have integrated the availability analysis to the model described above. The availability of a system is analyzed by Monte Carlo simulation, which involves the modeling of the probabilities of failure, the type of failure, the time to repair associated with each failure, and time of occurrence for a field system. The availability model performed reduces significantly the uncertainties on a multi-period planning production of either oil or gas, predicting the probability of failure and the downtime related to the hydrocarbon production through its lifetime. In this study, the unavailability of the equipment was quantified, reporting a subsea equipment downtime of approximately 7%. As a result, new production planning is accomplished in the effective work period, which will be beneficial in financial risk decisions such as a government’s deliverability contracts.

Current parallelizing compilers cannot identify a significant fraction of parallelizable loops because they have complex or statically insufficiently defined access patterns. In our previous work, we have speculatively executed a loop as a doall, and applied a fully parallel data dependence test to determine if it had any cross–processor depen- dences. If the test failed, then the loop was re–executed serially. While this method exploits doall parallelism well, it can cause slowdowns for loops with even one cross- processor flow dependence because we have to re-execute sequentially. Moreover, the existing, partial parallelism of loops is not exploited. We demonstrate a generalization of the speculative doall parallelization tech- nique, called the Recursive LRPD test, that can extract and exploit the maximum available parallelism of any loop and that limits potential slowdowns to the over- head of the run-time dependence test itself. In this thesis, we have presented the base algorithm and an analysis of the different heuristics for its practical applica- tion. To reduce the run-time overhead of the Recursive LRPD test, we have im- plemented on-demand checkpointing and commit, more efficient data dependence analysis and shadow structures, and feedback-guided load balancing. We obtained scalable speedups for loops from Track, Spice, and FMA3D that were not paralleliz- able by previous speculative parallelization methods.

Optimization of waterflooding sweep efficiency has been widely applied in reservoir engineering to improve hydrocarbon recovery while delaying water breakthrough and minimizing the bypassed oil in reservoirs. We develop a new framework to optimize flooding sweep efficiency in geologic formations with heterogeneous properties and demonstrate its application to waterflooding and geological CO2 sequestration problems. The new method focuses on equalizing and delaying (under constant total injected volume) the breakthrough time of the injected fluid at production wells. For application to CO2 sequestration where producers may not be present, we introduce the concept of pseudo production wells that have insignificant production rates (with negligible effect on the overall flow regime) for quantification of hypothetical breakthrough curves that can be used for optimization purpose. We apply the new method to waterflooding and CO2 sequestration optimization using two heterogeneous reservoir models. We show that in water flooding experiments, the proposed method improves the sweep efficiency by delaying the field breakthrough and equalizing breakthrough times in all production wells. In this case, the optimization results in increased oil recovery and decreased water production. We apply a modified version of the proposed algorithm to geologic CO2 sequestration problems to maximize the storage capacity of aquifers by enhancing the residual and dissolution trapping. The results from applying the proposed approach to optimization of geologic CO2 storage problems illustrate the effectiveness of the algorithm in improving residual and solubility trapping by increasing the contact between available fresh brine and the injected CO2 plume through a more uniform distribution of CO2 in the aquifer.

In this thesis, the feasibility of inverse evaluation of piezoelectric parameters by using piezoelectric beam¡¦s natural frequencies is presented. Generally, all the piezoelectric parameters are not measured simultaneously. In other words, the interactive effects between the piezoelectricity and strain are not all included. The piezoelectric beam¡¦s natural frequencies are analyzed by the inverse evaluation of piezoelectric parameters associated with the optimization algorithm. The dynamic model for piezoelectric beam is proposed by using Hamilton¡¦s principle in this study. The corresponding eigenvalue problems of different piezoelectric beams are formulated and solved by employing differential quadrature method (DQM). The genetic optimization algorithm is employed to optimize all piezoelectric parameters from the measured natural frequencies. Optimization of piezoelectric parameters of piezoelectric beam under the boundary conditions of fix-fix ends and fix-free ends are investigated. The robustness of program is also demonstrated through several numerical examples. The results demonstrate the method of this study applied to determine piezoelectric parameters by using natural frequencies is feasible and practicable.

In this dissertation, we propose area-efficient Advanced Encryption Standard (AES) processor designs by applying four new common-subexpression-elimination (CSE) algorithms to the sub-functions that realize the various transformations in AES encryption and decryption. The first category of sub-functions is derived by combining adjacent transformations in each AES round into a new transformation. The other category of sub-functions is from the integrated transformations in the AES encryption and decryption process with shared common operations. Then the proposed bit-level CSE algorithm reduces further the area cost of realizing the sub-functions by extracting the common factors in the bit-level expressions of these sub-functions. The separate area-reduction effects of combinations, integrations and CSE optimization mentioned above are analyzed in order to examine the efficiency of each technique. Cell-based implementation results show that the area reduction rates of the AES processors with our proposed CSE methods achieve significant area improvement compared with Synopsys optimization results.

In the year of 1990, the BEMS concept has been developed into an open web-based structure where internet has been utilized widely. Universally adapted communication protocols, such as BACNet and Lonworks have been applied in engineering practices and created huge opportunity for building energy conservation designs. In this research, the development of the BEMS system has been discussed and analyzed in detail including its hardware infrastructure and software requirements, so that the HVAC , lighting and power subsystems can be integrated while remote control can be achieved. The classification of BEMS system, to be adapted in Taiwan, has been proposed and published by the end of year 2006, which enables real-time online building energy auditing with energy conservation potential assessed. Design examples, such as general hospitals, department stores, hotels, have been selected and analyzed to demonstrate the feasibility in adapting BEMS system in Taiwan with successful results.

The optimizations of air-condition systems using naturalrefrigerants R-600a are studied in this thesis. The theories including the exergy analysis, heat transfer and fluid mechanics are combined together to study the exergy transfer and destroy in each component. The optimizing parameters in this research include cooling air velocities, the tube diameters of evaporator and condenser. If all the conditions remain constant expect the tube diameter of evaporator, the numerical results display that the values of the total entropy generation rate with R-600a decrease from tube diameter 1.0 cm to 1.1 cm and increase from 1.1 cm to 1.2 cm. The tube diameter of evaporator and condenser at a minimum value of total entropy generation rate is 1.1cm for the simulation conditions. Besides, the coefficient of performance and the energy efficiency ratios also have maximum values at the tube diameter. If all the conditions remain constant expect the tube diameters of condenser, the tendencies of total entropy generation rate arethe same as those in evaporator.