A. Bassam

Modeling and optimization of a solar parabolic trough concentrator system using inverse artificial neural network

In this paper, an artificial neural network inverse (ANNi) model is applied to optimize the thermal performance (η) of parabolic trough concentrators. A feedforward neural network architecture is trained using an experimental database from parabolic trough concentrators operations. Rim angle ( φr), inlet (Tin) and outlet (Tout) fluid temperatures, ambient temperature (Ta), water flow (Fw), direct solar radiation (Gb), and the wind velocity (Vw) were used as main input variables within the neural network model to estimate the thermal performance with a correlation coefficient of R2 = 0.9996 between experimental and simulated values. The sensitivity analysis is carried out to verify the effect of all input variables. The optimal operation conditions of parabolic trough concentrators are established using artificial neural network inverse modeling (ANNi) to achieve optimal operation conditions of parabolic trough concentrators. The results indicated that ANNi is a feasible tool for Parabolic Trough Concentra...

Photovoltaic Module Temperature Estimation: A Comparison Between Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems Models

The main objective of this paper is to present a comparison between two models for estimation of a photovoltaic system’s module temperature (T\(_{mod}\)) using Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems. Both estimations use measurements of common operation variables: current, voltage and duty cycle (d) from a power converter of the photovoltaic system as input variables and T\(_{mod}\) as a desired output. The models used the same database for the training process, different training strategies were evaluated with the objective to find which model has the best estimation with respect to the T\(_{mod}\). Subsequently, the output results from these architectures are validated via the Root Mean Squared Error, Mean Absolute Percentage Error and correlation coefficient. Results show that the Artificial Neural Network model in comparison with Adaptive Neuro Fuzzy Inference System model provides a better estimation of T\(_{mod}\) with \(R = 0.8167\). Developed models may have an application with smart sensors on cooling systems for photovoltaic modules with the objective of improving their operation efficiency.

Thermal Efficiency Prediction of a Solar Low Enthalpy Steam Generating Plant Employing Artificial Neural Networks

The present paper describes a mathematical model based on application of Artificial Neural Networks (ANN) employing a Multi-Layer Perceptron (MLP) model for thermal efficiency prediction of a solar low enthalpy steam generation plant composed by a Parabolic Trough Collector (PTCs) array. The MLP model uses physical data measurement in the steam prssoduction for heat processes. The input parameters used to achieve the prediction of thermal efficiency where: inlet and outlet working fluid temperature, flow working fluid, ambient temperature, direct solar radiation and wind velocity. After several training, the best MLP architecture was obtained employing Levenberg-Marquardt optimization algorithm, the logarithmic sigmoid transfer-function and the linear transfer-function for the hidden and output layer; and four neurons at the hidden layer, which predicts the thermal efficiency with a satisfactory determination coefficient (R\(^{\text {2}}\) = 0.99996). The predictive model can be implemented at intelligent sensors that allow to improve control of the PTCs system and leads to better utilization of the solar resource.

Flexibility in Biopharmaceutical Manufacturing Using Particle Swarm Algorithms and Genetic Algorithms

Pharmaceutical researchers and biotechnology companies are devoted to developing medicines, such as: therapeutic proteins, human insulin , vaccines for hepatitis , food grade protein, chymosin detergent enzyme, and cryophilic protease. This allows patients to live longer, healthier, and more productive. Within this context, there is a high degree of consensus in the biomanufacturing industry that product quality, customer service, and cost efficiency are fundamental for success. Based on our knowledge there has not been an adequate flexibility strategy to manufacture different multiproduct drug substances, such as designing a plant, determining the number of units for a specific task, assigning raw materials to different production processes, and deciding the production planning. The aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage of multiproduct batch plant design (MBPD). For this purpose, it is proposed to solve the problem in two different ways: the first way is by using a particle swarm algorithm (PSA) and the second way is by a genetic algorithm (GA). This paper presents the effectiveness and performance comparison of PSA and GA for optimal design of a MBPD. The experimental results (given by investment cost, number and size of equipment, computational time, and idle times within the plant) obtained by the GA are better than those found by the PSA. This methodology can help the decision makers, and constitutes a very promising framework for finding a set of good solutions.

Deep Learning for Photovoltaic Power Plant Forecasting

Deep Learning is getting a relative importance in the field of machine learning due to better performance in fields of classification and pattern recognition. However, deep models have seen little use in time series forecasting. Thus, the purpose of this work is to investigate the performance of such models in power plant output forecasting. A classical Artificial Neural Network with one hidden layer and two Deep Learning models were developed to forecast the output from a photovoltaic power plant. A Recurrent Deep Neural Network with Long Short Term Memory and a Deep Neural Network were proposed to predict future values; trained by the Adam algorithm and validated using R, RMSE and MAPE statistical criteria. Using deep models improves the accuracy of forecasting better than models without a large hidden layer size. This improvement is demonstrated by training several structures of Deep Models and feed forward Neural Networks models. Correlation coefficient of 1.0 is achieved using a deep architecture for this case study.

An Approach to Codification Power on the Behavior of Genetic Algorithms

Genetics Algorithms (GAs) are based on the principles of Darwins evolution which are applied to the minimization complex function successfully. Codification is a very important issue when GAs are designed to dealing with a combinatorial problem. An effective crossed binary method is developed. The GAs have the advantages of no special demand for initial values of decision variables, lower computer storage, and less CPU time for computation. Better results are obtained in comparison the results of traditional Genetic Algorithms. The effectiveness of GAs with crossed binary coding in minimizing the complex function is demonstrated.

Optimization operation of a parabolic trough collector using artificial neural network

The present work describes the thermal efficiency optimization of parabolic trough collectors by combining a model of artificial neural network and computational optimization techniques. A feedforward neural network architecture is trained using experimental database from parabolic trough collector operations. Rim angle, inlet and outlet fluid temperatures, ambient temperature, water flow, direct solar radiation, and wind velocity were used as main input variables within the neural network model to estimate the thermal performance. The optimal operation conditions of parabolic trough collectors are established using the hybridization of optimization technique with neural network model to achieve optimal operation conditions of parabolic trough collector. The result indicated that methodology implemented is a feasible tool for parabolic trough collectors optimization.