46,838 research outputs found
A multi-objective genetic algorithm for the design of pressure swing adsorption
Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA
processes would be beneficial for the development of the technology, but their development is
a difficult task due to the complexity of the simulation of PSA cycles and the computational
effort needed to detect the performance at cyclic steady state.
We present a preliminary investigation of the performance of a custom multi-objective genetic
algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of
air for N2 production. The simulation requires a detailed diffusion model, which involves coupled
nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA
to handle this complex problem has been assessed by comparison with direct search methods.
An analysis of the effect of MOGA parameters on the performance is also presented
Temporal Feature Selection with Symbolic Regression
Building and discovering useful features when constructing machine learning models is the central task for the machine learning practitioner. Good features are useful not only in increasing the predictive power of a model but also in illuminating the underlying drivers of a target variable. In this research we propose a novel feature learning technique in which Symbolic regression is endowed with a ``Range Terminal\u27\u27 that allows it to explore functions of the aggregate of variables over time. We test the Range Terminal on a synthetic data set and a real world data in which we predict seasonal greenness using satellite derived temperature and snow data over a portion of the Arctic. On the synthetic data set we find Symbolic regression with the Range Terminal outperforms standard Symbolic regression and Lasso regression. On the Arctic data set we find it outperforms standard Symbolic regression, fails to beat the Lasso regression, but finds useful features describing the interaction between Land Surface Temperature, Snow, and seasonal vegetative growth in the Arctic
Spring search algorithm for simultaneous placement of distributed generation and capacitors
Purpose. In this paper, for simultaneous placement of distributed generation (DG) and capacitors, a new approach based on Spring Search Algorithm (SSA), is presented. This method is contained two stages using two sensitive index Sv and Ss. Sv and Ss are calculated according to nominal voltageand network losses. In the first stage, candidate buses are determined for installation DG and capacitors according to Sv and Ss. Then in the second stage, placement and sizing of distributed generation and capacitors are specified using SSA. The spring search algorithm is among the optimization algorithms developed by the idea of laws of nature and the search factors are a set of objects. The proposed algorithm is tested on 33-bus and 69-bus radial distribution networks. The test results indicate good performance of the proposed methodЦель. В статье для одновременного размещения распределенной генерации и конденсаторов представлен новый подход, основанный на "пружинном" алгоритме поиска (Spring Search Algorithm, SSA). Данный метод состоит из двух этапов с использованием двух показателей чувствительности Sv и Ss. Показатели чувствительности Sv и Ss рассчитываются в соответствии с номинальным напряжением и потерями в сети. На первом этапе определяются шины-кандидаты для установки распределенной генерации и конденсаторов согласно Sv и Ss. Затем, на втором этапе размещение и калибровка распределенной генерации и конденсаторов выполняются с использованием алгоритма SSA. "Пружинный" алгоритм поиска входит в число алгоритмов оптимизации, разработанных на основе идей законов природы, а факторы поиска представляют собой набор объектов. Предлагаемый алгоритм тестируется на радиальных распределительных сетях с 33 и 69 шинами. Результаты тестирования показывают хорошую эффективность предложенного метода
Computational inference beyond Kingman's coalescent
Full likelihood inference under Kingman's coalescent is a computationally challenging problem to which importance sampling (IS) and the product of approximate conditionals (PAC) method have been applied successfully. Both methods can be expressed in terms of families of intractable conditional sampling distributions (CSDs), and rely on principled approximations for accurate inference. Recently, more general Λ- and Ξ- coalescents have been observed to provide better modelling ts to some genetic data sets. We derive families of approximate CSDs for nite sites Λ- and Ξ-coalescents, and use them to obtain "approximately optimal" IS and PAC algorithms for Λ coalescents, yielding substantial gains in efficiency over existing methods
Optimal design and optimal control of structures undergoing finite rotations and elastic deformations
In this work we deal with the optimal design and optimal control of
structures undergoing large rotations. In other words, we show how to find the
corresponding initial configuration and the corresponding set of multiple load
parameters in order to recover a desired deformed configuration or some
desirable features of the deformed configuration as specified more precisely by
the objective or cost function. The model problem chosen to illustrate the
proposed optimal design and optimal control methodologies is the one of
geometrically exact beam. First, we present a non-standard formulation of the
optimal design and optimal control problems, relying on the method of Lagrange
multipliers in order to make the mechanics state variables independent from
either design or control variables and thus provide the most general basis for
developing the best possible solution procedure. Two different solution
procedures are then explored, one based on the diffuse approximation of
response function and gradient method and the other one based on genetic
algorithm. A number of numerical examples are given in order to illustrate both
the advantages and potential drawbacks of each of the presented procedures.Comment: 35 pages, 11 figure
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