Optimization of wastewater quality in the Sines refinery

In order to address and resolve the wastewater contamination problem of the Sines refinery with the main objective of optimizing the quality of this stream and reducing the costs charged to the refinery, a dynamic mass balance was developed nd implemented for ammonia and polar oil and grease (O&G) contamination in the wastewater circuit. The inadequate routing of sour gas from the sour water stripping unit and the kerosene caustic washing unit, were identified respectively as the major source of ammonia and polar substances present in the industrial wastewater effluent. For the O&G content, a predictive model was developed for the kerosene caustic washing unit, following the Projection to Latent Structures (PLS) approach. Comparison between analytical data for ammonia and polar O&G concentrations in refinery wastewater originating from the Dissolved Air Flotation (DAF) effluent and the model predictions of the dynamic mass balance calculations are in a very good agreement and highlights the dominant impact of the identified streams for the wastewater contamination levels. The ammonia contamination problem was solved by rerouting the sour gas through an existing clogged line with ammonia salts due to a non-insulated line section, while for the O&G a dynamic mass balance was implemented as an online tool, which allows for prevision of possible contamination situations and taking the required preventive actions, and can also serve as a basis for establishing relationships between the O&G contamination in the refinery wastewater with the properties of the refined crude oils and the process operating conditions. The PLS model developed could be of great asset in both optimizing the existing and designing new refinery wastewater treatment units or reuse schemes. In order to find a possible treatment solution for the spent caustic problem, an on-site pilot plant experiments for NaOH recovery from the refinery kerosene caustic washing unit effluent using an alkaline-resistant nanofiltration (NF) polymeric membrane were performed in order to evaluate its applicability for treating these highly alkaline and contaminated streams. For a constant operating pressure and temperature and adequate operating conditions, 99.9% of oil and grease rejection and 97.7% of chemical oxygen demand (COD) rejection were observed. No noticeable membrane fouling or flux decrease were registered until a volume concentration factor of 3. These results allow for NF permeate reuse instead of fresh caustic and for significant reduction of the wastewater contamination, which can result in savings of 1.5 M€ per year at the current prices for the largest Portuguese oil refinery. The capital investments needed for implementation of the required NF membrane system are less than 10% of those associated with the traditional wet air oxidation solution of the spent caustic problem. The operating costs are very similar, but can be less than half if reusing the NF concentrate in refinery pH control applications. The payback period was estimated to be 1.1 years. Overall, the pilot plant experimental results obtained and the process economic evaluation data indicate a very competitive solution through the proposed NF treatment process, which represents a highly promising alternative to conventional and existing spent caustic treatment units.

Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flowcomputation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we develop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow computation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation.

Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

How Much Does It Cost? Optimization of Costs in Sequence Analysis of Social Science Data

One major methodological problem in analysis of sequence data is the determination of costs from which distances between sequences are derived. Although this problem is currently not optimally dealt with in the social sciences, it has some similarity with problems that have been solved in bioinformatics for three decades. In this article, the authors propose an optimization of substitution and deletion/insertion costs based on computational methods. The authors provide an empirical way of determining costs for cases, frequent in the social sciences, in which theory does not clearly promote one cost scheme over another. Using three distinct data sets, the authors tested the distances and cluster solutions produced by the new cost scheme in comparison with solutions based on cost schemes associated with other research strategies. The proposed method performs well compared with other cost-setting strategies, while it alleviates the justification problem of cost schemes.

Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flow computation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we de- velop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional mul- tilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrec- tional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimiza- tion search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow com- putation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation.

Selection of funding schemes by a borrowing decision model: a Hong Kong case study

In financial decision-making, a number of mathematical models have been developed for financial management in construction. However, optimizing both qualitative and quantitative factors and the semi-structured nature of construction finance optimization problems are key challenges in solving construction finance decisions. The selection of funding schemes by a modified construction loan acquisition model is solved by an adaptive genetic algorithm (AGA) approach. The basic objectives of the model are to optimize the loan and to minimize the interest payments for all projects. Multiple projects being undertaken by a medium-size construction firm in Hong Kong were used as a real case study to demonstrate the application of the model to the borrowing decision problems. A compromise monthly borrowing schedule was finally achieved. The results indicate that Small and Medium Enterprise (SME) Loan Guarantee Scheme (SGS) was first identified as the source of external financing. Selection of sources of funding can then be made to avoid the possibility of financial problems in the firm by classifying qualitative factors into external, interactive and internal types and taking additional qualitative factors including sovereignty, credit ability and networking into consideration. Thus a more accurate, objective and reliable borrowing decision can be provided for the decision-maker to analyse the financial options.

A robust controller design method for feedback substitution schemes using genetic algorithms

Controllers for feedback substitution schemes demonstrate a trade-off between noise power gain and normalized response time. Using as an example the design of a controller for a radiometric transduction process subjected to arbitrary noise power gain and robustness constraints, a Pareto-front of optimal controller solutions fulfilling a range of time-domain design objectives can be derived. In this work, we consider designs using a loop shaping design procedure (LSDP). The approach uses linear matrix inequalities to specify a range of objectives and a genetic algorithm (GA) to perform a multi-objective optimization for the controller weights (MOGA). A clonal selection algorithm is used to further provide a directed search of the GA towards the Pareto front. We demonstrate that with the proposed methodology, it is possible to design higher order controllers with superior performance in terms of response time, noise power gain and robustness.

Trade-offs and responsiveness of the single-layer urban canopy parametrization in WRF: an offline evaluation using the MOSCEM optimization algorithm and field observations

For an increasing number of applications, mesoscale modelling systems now aim to better represent urban areas. The complexity of processes resolved by urban parametrization schemes varies with the application. The concept of fitness-for-purpose is therefore critical for both the choice of parametrizations and the way in which the scheme should be evaluated. A systematic and objective model response analysis procedure (Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm) is used to assess the fitness of the single-layer urban canopy parametrization implemented in the Weather Research and Forecasting (WRF) model. The scheme is evaluated regarding its ability to simulate observed surface energy fluxes and the sensitivity to input parameters. Recent amendments are described, focussing on features which improve its applicability to numerical weather prediction, such as a reduced and physically more meaningful list of input parameters. The study shows a high sensitivity of the scheme to parameters characterizing roof properties in contrast to a low response to road-related ones. Problems in partitioning of energy between turbulent sensible and latent heat fluxes are also emphasized. Some initial guidelines to prioritize efforts to obtain urban land-cover class characteristics in WRF are provided. Copyright © 2010 Royal Meteorological Society and Crown Copyright.

Multilayer optimization in radio resource allocation for the packet transmission in wireless networks

In the last decade mobile wireless communications have witnessed an explosive growth in the user’s penetration rate and their widespread deployment around the globe. It is expected that this tendency will continue to increase with the convergence of fixed Internet wired networks with mobile ones and with the evolution to the full IP architecture paradigm. Therefore mobile wireless communications will be of paramount importance on the development of the information society of the near future. In particular a research topic of particular relevance in telecommunications nowadays is related to the design and implementation of mobile communication systems of 4th generation. 4G networks will be characterized by the support of multiple radio access technologies in a core network fully compliant with the Internet Protocol (all IP paradigm). Such networks will sustain the stringent quality of service (QoS) requirements and the expected high data rates from the type of multimedia applications to be available in the near future. The approach followed in the design and implementation of the mobile wireless networks of current generation (2G and 3G) has been the stratification of the architecture into a communication protocol model composed by a set of layers, in which each one encompasses some set of functionalities. In such protocol layered model, communications is only allowed between adjacent layers and through specific interface service points. This modular concept eases the implementation of new functionalities as the behaviour of each layer in the protocol stack is not affected by the others. However, the fact that lower layers in the protocol stack model do not utilize information available from upper layers, and vice versa, downgrades the performance achieved. This is particularly relevant if multiple antenna systems, in a MIMO (Multiple Input Multiple Output) configuration, are implemented. MIMO schemes introduce another degree of freedom for radio resource allocation: the space domain. Contrary to the time and frequency domains, radio resources mapped into the spatial domain cannot be assumed as completely orthogonal, due to the amount of interference resulting from users transmitting in the same frequency sub-channel and/or time slots but in different spatial beams. Therefore, the availability of information regarding the state of radio resources, from lower to upper layers, is of fundamental importance in the prosecution of the levels of QoS expected from those multimedia applications. In order to match applications requirements and the constraints of the mobile radio channel, in the last few years researches have proposed a new paradigm for the layered architecture for communications: the cross-layer design framework. In a general way, the cross-layer design paradigm refers to a protocol design in which the dependence between protocol layers is actively exploited, by breaking out the stringent rules which restrict the communication only between adjacent layers in the original reference model, and allowing direct interaction among different layers of the stack. An efficient management of the set of available radio resources demand for the implementation of efficient and low complexity packet schedulers which prioritize user’s transmissions according to inputs provided from lower as well as upper layers in the protocol stack, fully compliant with the cross-layer design paradigm. Specifically, efficiently designed packet schedulers for 4G networks should result in the maximization of the capacity available, through the consideration of the limitations imposed by the mobile radio channel and comply with the set of QoS requirements from the application layer. IEEE 802.16e standard, also named as Mobile WiMAX, seems to comply with the specifications of 4G mobile networks. The scalable architecture, low cost implementation and high data throughput, enable efficient data multiplexing and low data latency, which are attributes essential to enable broadband data services. Also, the connection oriented approach of Its medium access layer is fully compliant with the quality of service demands from such applications. Therefore, Mobile WiMAX seems to be a promising 4G mobile wireless networks candidate. In this thesis it is proposed the investigation, design and implementation of packet scheduling algorithms for the efficient management of the set of available radio resources, in time, frequency and spatial domains of the Mobile WiMAX networks. The proposed algorithms combine input metrics from physical layer and QoS requirements from upper layers, according to the crosslayer design paradigm. Proposed schedulers are evaluated by means of system level simulations, conducted in a system level simulation platform implementing the physical and medium access control layers of the IEEE802.16e standard.

FBG optimization using spline encoded evolution strategy

ABSTRACT: This paper presents an encoding scheme adapted for Fiber Bragg Grating (FBG) optimization using metaheuristics. The proposed encoding scheme uses spline approximations in order to build softened refractive index profiles from few encoded parameters. This approach is suitable for Fiber Bragg Grating (FBG) synthesis because it ensures both the reduction of the problem dimensionality and the respect of important restrictions associated to the FBG manufacture. Simulations are shown where an ES using the spline encoding was able to converge faster and produce more interesting filters, when compared with conventional encoding schemes.

Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

Characterization and optimization of an x-ray laser for the spectroscopy of Li-like heavy-ions

Recent developments in the theory of plasma-based collisionally excited x-ray lasers (XRL) have shown an optimization potential based on the dependence of the absorption region of the pumping laser on its angle of incidence on the plasma. For the experimental proof of this idea, a number of diagnostic schemes were developed, tested, qualified and applied. A high-resolution imaging system, yielding the keV emission profile perpendicular to the target surface, provided positions of the hottest plasma regions, interesting for the benchmarking of plasma simulation codes. The implementation of a highly efficient spectrometer for the plasma emission made it possible to gain information about the abundance of the ionization states necessary for the laser action in the plasma. The intensity distribution and deflection angle of the pump laser beam could be imaged for single XRL shots, giving access to its refraction process within the plasma. During a European collaboration campaign at the Lund Laser Center, Sweden, the optimization of the pumping laser incidence angle resulted in a reduction of the required pumping energy for a Ni-like Mo XRL, which enabled the operation at a repetition rate of 10 Hz. Using the experiences gained there, the XRL performance at the PHELIX facility, GSI Darmstadt with respect to achievable repetition rate and at wavelengths below 20 nm was significantly improved, and also important information for the development towards multi-100 eV plasma XRLs was acquired. Due to the setup improvements achieved during the work for this thesis, the PHELIX XRL system now has reached a degree of reproducibility and versatility which is sufficient for demanding applications like the XRL spectroscopy of heavy ions. In addition, a European research campaign, aiming towards plasma XRLs approaching the water-window (wavelengths below 5 nm) was initiated.

Mathematical Optimization Applied to Thermal and Electrical Energy Systems

This Thesis aims at building and discussing mathematical models applications focused on Energy problems, both on the thermal and electrical side. The objective is to show how mathematical programming techniques developed within Operational Research can give useful answers in the Energy Sector, how they can provide tools to support decision making processes of Companies operating in the Energy production and distribution and how they can be successfully used to make simulations and sensitivity analyses to better understand the state of the art and convenience of a particular technology by comparing it with the available alternatives. The first part discusses the fundamental mathematical background followed by a comprehensive literature review about mathematical modelling in the Energy Sector. The second part presents mathematical models for the District Heating strategic network design and incremental network design. The objective is the selection of an optimal set of new users to be connected to an existing thermal network, maximizing revenues, minimizing infrastructure and operational costs and taking into account the main technical requirements of the real world application. Results on real and randomly generated benchmark networks are discussed with particular attention to instances characterized by big networks dimensions. The third part is devoted to the development of linear programming models for optimal battery operation in off-grid solar power schemes, with consideration of battery degradation. The key contribution of this work is the inclusion of battery degradation costs in the optimisation models. As available data on relating degradation costs to the nature of charge/discharge cycles are limited, we concentrate on investigating the sensitivity of operational patterns to the degradation cost structure. The objective is to investigate the combination of battery costs and performance at which such systems become economic. We also investigate how the system design should change when battery degradation is taken into account.

Computational Schemes for Optimizing Domains of Attraction in Dynamical Systems

In this paper, several computational schemes are presented for the optimal tuning of the global behavior of nonlinear dynamical sys- tems. Specifically, the maximization of the size of domains of attraction associated with invariants in parametrized dynamical sys- tems is addressed. Cell Mapping (CM) tech- niques are used to estimate the size of the domains, and such size is then maximized via different optimization tools. First, a ge- netic algorithm is tested whose performance shows to be good for determining global maxima at the expense of high computa- tional cost. Secondly, an iterative scheme based on a Stochastic Approximation proce- dure (the Kiefer-Wolfowitz algorithm) is eval- uated showing acceptable performance at low cost. Finally, several schemes combining neu- ral network based estimations and optimiza- tion procedures are addressed with promising results. The performance of the methods is illus- trated with two applications: first on the well-known van der Pol equation with stan- dard parametrization, and second the tuning of a controller for saturated systems.

Score optimization and template updating in a biometric technique for authentication in mobiles based on gestures

This article focuses on the evaluation of a biometric technique based on the performance of an identifying gesture by holding a telephone with an embedded accelerometer in his/her hand. The acceleration signals obtained when users perform gestures are analyzed following a mathematical method based on global sequence alignment. In this article, eight different scores are proposed and evaluated in order to quantify the differences between gestures, obtaining an optimal EER result of 3.42% when analyzing a random set of 40 users of a database made up of 80 users with real attempts of falsification. Moreover, a temporal study of the technique is presented leeding to the need to update the template to adapt the manner in which users modify how they perform their identifying gesture over time. Six updating schemes have been assessed within a database of 22 users repeating their identifying gesture in 20 sessions over 4 months, concluding that the more often the template is updated the better and more stable performance the technique presents.