19,956 research outputs found
Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
In this work, a nonlinear model predictive controller is developed for a
batch polymerization process. The physical model of the process is
parameterized along a desired trajectory resulting in a trajectory linearized
piecewise model (a multiple linear model bank) and the parameters are
identified for an experimental polymerization reactor. Then, a multiple model
adaptive predictive controller is designed for thermal trajectory tracking of
the MMA polymerization. The input control signal to the process is constrained
by the maximum thermal power provided by the heaters. The constrained
optimization in the model predictive controller is solved via genetic
algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference
Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)
This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potential of using multivariate NIR calibration models for seed classification was demonstrated using filled viable and non-viable seeds that could be separated with an accuracy of 98-99%. It was also shown that multivariate NIR calibration models gave low errors (0.7% and 1.9%) in prediction of seed moisture content for bulk seed and single seeds, respectively, using either NIR reflectance or transmittance spectroscopy. Genetic algorithms selected three to eight wavelength bands in the NIR region and these narrow bands gave about the same prediction of seed moisture content (0.6% and 1.7%) as using the whole NIR interval in the PLS regression models. The selected regions were simulated as NIR filters in OLS regression resulting in predictions of the same quality (0.7 % and 2.1%). This finding opens possibilities to apply NIR sensors in fast and simple spectrometers for the determination of seed moisture content. Near infrared (NIR) radiation interacts with overtones of vibrating bonds in polar molecules. The resulting spectra contain chemical and physical information. This offers good possibilities to measure seed-water interactions, but also to interpret processes within seeds. It is shown that seed-water interaction involves both transitions and changes mainly in covalent bonds of O-H, C-H, C=O and N-H emanating from ongoing physiological processes like seed respiration and protein metabolism. I propose that BPLS analysis that has orthonormal loadings and orthogonal scores giving the same predictions as using conventional PLS regression, should be used as a standard to harmonise the interpretation of NIR spectra
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
Using neutral cline decay to estimate contemporary dispersal: a generic tool and its application to a major crop pathogen
Dispersal is a key parameter of adaptation, invasion and persistence. Yet standard population genetics inference methods hardly distinguish it from drift and many species cannot be studied by direct mark-recapture methods. Here, we introduce a method using rates of change in cline shapes for neutral markers to estimate contemporary dispersal. We apply it to the devastating banana pest Mycosphaerella fijiensis, a wind-dispersed fungus for which a secondary contact zone had previously been detected using landscape genetics tools. By tracking the spatio-temporal frequency change of 15 microsatellite markers, we find that σ, the standard deviation of parent–offspring dispersal distances, is 1.2 km/generation1/2. The analysis is further shown robust to a large range of dispersal kernels. We conclude that combining landscape genetics approaches to detect breaks in allelic frequencies with analyses of changes in neutral genetic clines offers a powerful way to obtain ecologically relevant estimates of dispersal in many species
Mutual benefits of two multicriteria analysis methodologies: A case study for batch plant design
This paper presents a MultiObjective Genetic Algorithm (MOGA) optimization framework for batch plant design. For this purpose, two approaches are implemented and compared with respect to three criteria, i.e., investment cost, equipment number and a flexibility indicator based on work in process (the so-called WIP) computed by use of a discrete-event simulation model. The first approach involves a genetic algorithm in order to generate acceptable solutions, from which the best ones are chosen by using a Pareto Sort algorithm. The second approach combines the previous Genetic Algorithm with a multicriteria analysis methodology, i.e., the Electre method in order to find the best solutions. The performances of the two procedures are studied for a large-size problem and a comparison between the procedures is then made
An Unsupervised Method for Estimating the Global Horizontal Irradiance from Photovoltaic Power Measurements
In this paper, we present a method to determine the global horizontal
irradiance (GHI) from the power measurements of one or more PV systems, located
in the same neighborhood. The method is completely unsupervised and is based on
a physical model of a PV plant. The precise assessment of solar irradiance is
pivotal for the forecast of the electric power generated by photovoltaic (PV)
plants. However, on-ground measurements are expensive and are generally not
performed for small and medium-sized PV plants. Satellite-based services
represent a valid alternative to on site measurements, but their space-time
resolution is limited. Results from two case studies located in Switzerland are
presented. The performance of the proposed method at assessing GHI is compared
with that of free and commercial satellite services. Our results show that the
presented method is generally better than satellite-based services, especially
at high temporal resolutions
DIFFERENTIAL EVOLUTION FOR OPTIMIZATION OF PID GAIN IN ELECTRICAL DISCHARGE MACHINING CONTROL SYSTEM
ABSTRACT
PID controller of servo control system maintains the gap between Electrode and workpiece in Electrical Dis- charge Machining (EDM). Capability of the controller is significant since machining process is a stochastic phenomenon and physical behaviour of the discharge is unpredictable. Therefore, a Proportional Integral Derivative (PID) controller using Differential Evolution (DE) algorithm is designed and applied to an EDM servo actuator system in order to find suitable gain parameters. Simulation results verify the capabilities and effectiveness of the DE algorithm to search the best configuration of PID gain to maintain the electrode position.
Keywords: servo control system; electrical discharge machining; proportional integral derivative; con- troller tuning; differential evolution
On the Selection of Tuning Methodology of FOPID Controllers for the Control of Higher Order Processes
In this paper, a comparative study is done on the time and frequency domain
tuning strategies for fractional order (FO) PID controllers to handle higher
order processes. A new fractional order template for reduced parameter modeling
of stable minimum/non-minimum phase higher order processes is introduced and
its advantage in frequency domain tuning of FOPID controllers is also
presented. The time domain optimal tuning of FOPID controllers have also been
carried out to handle these higher order processes by performing optimization
with various integral performance indices. The paper highlights on the
practical control system implementation issues like flexibility of online
autotuning, reduced control signal and actuator size, capability of measurement
noise filtration, load disturbance suppression, robustness against parameter
uncertainties etc. in light of the above tuning methodologies.Comment: 27 pages, 10 figure
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