Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.

The importance of body-mass exponent optimization for evaluation of performance capability in cross-country skiing

Introduction Performance in cross-country skiing is influenced by the skier’s ability to continuously produce propelling forces and force magnitude in relation to the net external forces. A surrogate indicator of the “power supply” in cross-country skiing would be a physiological variable that reflects an important performance-related capability, whereas the body mass itself is an indicator of the “power demand” experienced by the skier. To adequately evaluate an elite skier’s performance capability, it is essential to establish the optimal ratio between the physiological variable and body mass. The overall aim of this doctoral thesis was to investigate the importance of body-mass exponent optimization for the evaluation of performance capability in cross-country skiing. Methods In total, 83 elite cross-country skiers (56 men and 27 women) volunteered to participate in the four studies. The physiological variables of maximal oxygen uptake (V̇O2max) and oxygen uptake corresponding to a blood-lactate concentration of 4 mmol∙l-1 (V̇O2obla) were determined while treadmill roller skiing using the diagonal-stride technique; mean oxygen uptake (V̇O2dp) and upper-body power output (Ẇ) were determined during double-poling tests using a ski-ergometer. Competitive performance data for elite male skiers were collected from two 15-km classical-technique skiing competitions and a 1.25-km sprint prologue; additionally, a 2-km double-poling roller-skiing time trial using the double-poling technique was used as an indicator of upper-body performance capability among elite male and female junior skiers. Power-function modelling was used to explain the race and time-trial speeds based on the physiological variables and body mass. Results The optimal V̇O2max-to-mass ratios to explain 15-km race speed were V̇O2max divided by body mass raised to the 0.48 and 0.53 power, and these models explained 68% and 69% of the variance in mean skiing speed, respectively; moreover, the 95% confidence intervals (CI) for the body-mass exponents did not include either 0 or 1. For the modelling of race speed in the sprint prologue, body mass failed to contribute to the models based on V̇O2max, V̇O2obla, and V̇O2dp. The upper-body power output-to-body mass ratio that optimally explained time-trial speed was Ẇ ∙ m-0.57 and the model explained 63% of the variance in speed. Conclusions The results in this thesis suggest that V̇O2max divided by the square root of body mass should be used as an indicator of performance in 15-km classical-technique races among elite male skiers rather than the absolute or simple ratio-standard scaled expression. To optimally explain an elite male skier’s performance capability in sprint prologues, power-function models based on oxygen-uptake variables expressed absolutely are recommended. Moreover, to evaluate elite junior skiers’ performance capabilities in 2-km double-poling roller-skiing time trials, it is recommended that Ẇ divided by the square root of body mass should be used rather than absolute or simple ratio-standard scaled expression of power output.

Optimization of cable cycles : a trade-off between reliability and cost

This paper elaborates the routing of cable cycle through available routes in a building in order to link a set of devices, in a most reasonable way. Despite of the similarities to other NP-hard routing problems, the only goal is not only to minimize the cost (length of the cycle) but also to increase the reliability of the path (in case of a cable cut) which is assessed by a risk factor. Since there is often a trade-off between the risk and length factors, a criterion for ranking candidates and deciding the most reasonable solution is defined. A set of techniques is proposed to perform an efficient and exact search among candidates. A novel graph is introduced to reduce the search-space, and navigate the search toward feasible and desirable solutions. Moreover, admissible heuristic length estimation helps to early detection of partial cycles which lead to unreasonable solutions. The results show that the method provides solutions which are both technically and financially reasonable. Furthermore, it is proved that the proposed techniques are very efficient in reducing the computational time of the search to a reasonable amount.

Using Multi-Objective Optimization To Maximize Multiple Benefits For Sustainable Drainage Design

For many years, drainage design was mainly about providing sufficient network capacity. This traditional approach had been successful with the aid of computer software and technical guidance. However, the drainage design criteria had been evolving due to rapid population growth, urbanisation, climate change and increasing sustainability awareness. Sustainable drainage systems that bring benefits in addition to water management have been recommended as better alternatives to conventional pipes and storages. Although the concepts and good practice guidance had already been communicated to decision makers and public for years, network capacity still remains a key design focus in many circumstances while the additional benefits are generally considered secondary only. Yet, the picture is changing. The industry begins to realise that delivering multiple benefits should be given the top priority while the drainage service can be considered a secondary benefit instead. The shift in focus means the industry has to adapt to new design challenges. New guidance and computer software are needed to assist decision makers. For this purpose, we developed a new decision support system. The system consists of two main components – a multi-criteria evaluation framework for drainage systems and a multi-objective optimisation tool. Users can systematically quantify the performance, life-cycle costs and benefits of different drainage systems using the evaluation framework. The optimisation tool can assist users to determine combinations of design parameters such as the sizes, order and type of drainage components that maximise multiple benefits. In this paper, we will focus on the optimisation component of the decision support framework. The optimisation problem formation, parameters and general configuration will be discussed. We will also look at the sensitivity of individual variables and the benchmark results obtained using common multi-objective optimisation algorithms. The work described here is the output of an EngD project funded by EPSRC and XP Solutions.

Optimization Of Groundwater Remediation With New Efficient Parallel Algorithm For Global Optimization

Application of optimization algorithm to PDE modeling groundwater remediation can greatly reduce remediation cost. However, groundwater remediation analysis requires a computational expensive simulation, therefore, effective parallel optimization could potentially greatly reduce computational expense. The optimization algorithm used in this research is Parallel Stochastic radial basis function. This is designed for global optimization of computationally expensive functions with multiple local optima and it does not require derivatives. In each iteration of the algorithm, an RBF is updated based on all the evaluated points in order to approximate expensive function. Then the new RBF surface is used to generate the next set of points, which will be distributed to multiple processors for evaluation. The criteria of selection of next function evaluation points are estimated function value and distance from all the points known. Algorithms created for serial computing are not necessarily efficient in parallel so Parallel Stochastic RBF is different algorithm from its serial ancestor. The application for two Groundwater Superfund Remediation sites, Umatilla Chemical Depot, and Former Blaine Naval Ammunition Depot. In the study, the formulation adopted treats pumping rates as decision variables in order to remove plume of contaminated groundwater. Groundwater flow and contamination transport is simulated with MODFLOW-MT3DMS. For both problems, computation takes a large amount of CPU time, especially for Blaine problem, which requires nearly fifty minutes for a simulation for a single set of decision variables. Thus, efficient algorithm and powerful computing resource are essential in both cases. The results are discussed in terms of parallel computing metrics i.e. speedup and efficiency. We find that with use of up to 24 parallel processors, the results of the parallel Stochastic RBF algorithm are excellent with speed up efficiencies close to or exceeding 100%.

A Diagnostic Assessment Of Evolutionary Multiobjective Optimization For Water Resources Systems

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with 4 or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are provided for which modern MOEAs should serve as tools and benchmarks in the future water resources literature.

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.

Optimization of osmotic dehydration of papaya followed by air-drying

FERNANDES, Fabiano A. N. et al. Optimization of Osmotic Dehydration of Papaya of followed by air-drying. Food Research Internation, v. 39, p. 492-498, 2006.

Growth optimization procedures for the phytopathogen Xylella fastidiosa

For the first time, growth curves are shown for the phytopathogen Xylella fastidiosa on traditional growth media such as PW (periwinkle wilt), BCYE (buffered charcoal yeast extract), and on new ones such as GYE (glutamate yeast extract) and PYE (phosphate yeast extract) that were developed in this work. The optimal growth conditions on solid and liquid media as well as their measurements are presented, by using total protein content and turbidity determinations. The results demonstrated that yeast extract provided sufficient nutrients for X. fastidiosa, since the cells grew well on PYE medium.