Multi-Response Optimization For Industrial Processes

Process optimization is a very important point in modern industry. There are many classical optimization methods, which can be applied when some mathematical conditions are verified. Real situations are not very simple so that classical methods may not succeed in optimizing; as in cases when the optimization has several contradictory objectives (Collette, 2002). The purpose of this work is to propose an optimization method for industrial processes with multiple inputs and multiple outputs (MIMO), for which the optimization objectives are generally contradictory and for which some objectives are not maximum or minimum but performance criteria. The first step of this method is modeling each process response by a quadratic model. After establishing the model, we use a simplified numerical optimization algorithm in order to determine values of the parameters allowing optimizing the different responses, for MIMO processes. This method will also allow finding optimum target values for multiple inputs single output processes

A Probabilistic Assessment Approach for Wind Turbine-Site Matching

This article provides a new methodology for wind turbine-site matching by using a probabilistic approach. The random behavior of the wind speed climate and the uncertainties of wind turbine characteristics are important to take into account in models used to evaluate the performance of the wind turbine. The proposed formulation of the wind turbine-site matching is derived based on the probabilistic reliability assessment approach. It was experimented using different power curve approximation models, for different random conditions, using time series of wind speed in two sites in Morocco: Dakhla and Essaouira. A comparison based on methods used in literature for the estimation of two-parameter of the Weibull function to fit the wind speed distribution is also carried out. The results revealed that the introduced performance indicators are less sensitive to the models used to approximate the wind power curves compared to the deterministic conventional indicator that leads to different rankings and problems of over-sizing or under-sizing. However, those performance indicators are more sensitive to the variation of the wind speed distribution parameter’s and can help on accurately estimate the wind power. Moreover, the proposed formulation allows a global sensitivity analysis using Sobol’s indices to observe the influence of each input parameter on the observed variances of the performance of a wind turbine. A numerical application illustrates the interpretation of sensitivity indices and shows the impact of the wind speed and the rated wind speed on the variance of the wind turbine performance. This method can help wind energy developers and manufacturers to optimally select WTGs for their future project and accurately forecast the performance of their WTGs for monitoring and maintenance scheduling under uncertainty

A Bayesian Regularized Artificial Neural Network for Up-Scaling Wind Speed Profile

Maximizing gains from wind energy potential is the principle objective of the wind power sector. Consequently, wind tower size is radically increasing. However, choosing an appropriate wind turbine for a selected site requires having an accurate estimation of vertical wind profile. This is also imperative from the cost and maintenance strategy point of view. Installing tall towers or other expensive devices such as LIDAR or SODAR raises the costs of a wind power project. In this work, we aim to investigate the ability of a Neural Network trained using the Bayesian Regularization technique to estimate wind speed profile up to a height of 100m based on knowledge of wind speed at lower heights. Results show that the proposed approach can achieve satisfactory predictions and prove the suitability of the proposed method for generating wind speed profile and probability distributions based on knowledge of wind speed at lower heights

Comparative analysis for wind resource assessment : Bayesian Regularization and Levenberg-Marquardt Training Algorithm for Neural Network

International audienc

Applied Bayesian regularized artificial neural network based approach for up-scaling wind speed profil and distribution

International audienc