Data-Driven Living Spaces' Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification.

Modeling and control of the heating feature of living spaces remain challenging tasks because of the intrinsic nonlinear nature of the involved processes as well as the strong nonlinearity of the entailed dynamic parameters in those processes. Although nowadays, adaptive heating controllers represent a crucial need for smart building energy management systems (SBEMS) as well as an appealing perspective for their effectiveness in optimizing energy efficiency, unfortunately, the leakage of models competent in handling the complexity of real living spaces' heating processes means the control strategies implemented in most SBEMSs are still conventional. Within this context and by considering that the living space's occupation rate (i.e., by users or residents) may affect the model and the issued heating control strategy of the concerned living space, we have investigated the design and implementation of a data-driven machine learning-based identification of the building's living space dynamic heating conduct, taking into account the occupancy (by the residents) of the heated space. In fact, the proposed modeling strategy takes advantage, on the one hand, of the forecasting capacity of the time-series of the nonlinear autoregressive exogenous (NARX) model, and on the other hand, from the multi-layer perceptron's (MLP) learning and generalization skills. The proposed approach has been implemented and applied for modeling the dynamic heating conduct of a real five-floor building's living spaces located at Senart Campus of University Paris-Est Créteil (UPEC), taking into account their occupancy (by users of this public building). The obtained results assessing the accuracy and addictiveness of the investigated hybrid machine learning-based approach are reported and discussed.

Fabrication of Polyamide6/Polyaniline as an Effective Nano-web Membrane for Removal of Cr (VI) from Water and a Black Box Approach in Modeling of Adsorption Process.

Chromium (Cr), as a highly toxic heavy metal ion, is still a severe environmental issue, although many research efforts have been put into its removal from water. Polyaniline (PANI), as a conductive polymer, demonstrated great capability in heavy metal adsorption due to its low cost, ease of synthesis, reversible redox behavior, and chemical stability. However, using PANI powder alone in heavy metal removal causes secondary pollution and aggregation in water. The PANI coating on a substrate could tackle this problem. In this study, polyaniline-coated polyamide6 (PA6/PANI) nano-web membrane was used for the removal of Cr(VI) in both adsorption and filtration-adsorption modes. The PA6/PANI nano-web membrane was fabricated via PA6 electrospinning followed by in-situ polymerization of the aniline monomer. The electrospinning condition of PA6 was optimized by the Taguchi method. The PA6/PANI nano-web membrane was characterized by FESEM, N2-adsorption/desorption, FT-IR, contact angle measurement, and tensile test. FT-IR and FESEM results demonstrated the successful synthesis of PA6/PANI nano-web and PANI homogeneous coating on PA6 nanofibers, respectively. The N2 adsorption/desorption results indicated that the pore volume of the PA6/PANI nano-web decreased by 39% compared to PA6 nanofibers. The tensile test and water contact angle studies showed that the coating of PANI on PA6 nanofibers improves the mechanical properties and hydrophilicity of PA6 by 10% and 25%, respectively. The application of PA6/PANI nano-web in the removal of Cr(VI) in batch and filtration modes exhibits excellent removal of 98.4 and 86.7%, respectively. A pseudo first order model well described the adsorption kinetics, and the adsorption isotherm was best fitted by the Langmuir model. A black box modeling approach based on artificial neural networks (ANN) was developed to predict the removal efficiency of the membrane. The superior performance of PA6/PANI in both adsorption and filtration-adsorption systems makes it a potential candidate for the removal of heavy metals from water on an industrial scale.

Real-Time Temperature Rise Estimation during Irreversible Electroporation Treatment through State-Space Modeling.

To evaluate the feasibility of real-time temperature monitoring during an electroporation-based therapy procedure, a data-driven state-space model was developed. Agar phantoms mimicking low conductivity (LC) and high conductivity (HC) tissues were tested under the influences of high (HV) and low (LV) applied voltages. Real-time changes in impedance, measured by Fourier Analysis SpecTroscopy (FAST) along with the known tissue conductivity and applied voltages, were used to train the model. A theoretical finite element model was used for external validation of the model, producing model fits of 95.8, 88.4, 90.7, and 93.7% at 4 mm and 93.2, 58.9, 90.0, and 90.1% at 10 mm for the HV-HC, LV-LC, HV-LC, and LV-HC groups, respectively. The proposed model suggests that real-time temperature monitoring may be achieved with good accuracy through the use of real-time impedance monitoring.

Forecasting Root-Zone Electrical Conductivity of Nutrient Solutions in Closed-Loop Soilless Cultures via a Recurrent Neural Network Using Environmental and Cultivation Information.

In existing closed-loop soilless cultures, nutrient solutions are controlled by the electrical conductivity (EC) of the solution. However, the EC of nutrient solutions is affected by both growth environments and crop growth, so it is hard to predict the EC of nutrient solution. The objective of this study was to predict the EC of root-zone nutrient solutions in closed-loop soilless cultures using recurrent neural network (RNN). In a test greenhouse with sweet peppers (Capsicum annuum L.), data were measured every 10 s from October 15 to December 31, 2014. Mean values for every hour were analyzed. Validation accuracy (R2) of a single-layer long short-term memory (LSTM) was 0.92 and root-mean-square error (RMSE) was 0.07, which were the best results among the different RNNs. The trained LSTM predicted the substrate EC accurately at all ranges. Test accuracy (R2) was 0.72 and RMSE was 0.08, which were lower than values for the validation. Deep learning algorithms were more accurate when more data were added for training. The addition of other environmental factors or plant growth data would improve model robustness. A trained LSTM can control the nutrient solutions in closed-loop soilless cultures based on predicted future EC. Therefore, the algorithm can make a planned management of nutrient solutions possible, reducing resource waste.