Hybrid Artificial Neural Network−Genetic Algorithm Technique for Modeling and Optimization of Plasma Reactor

Optimization of Position and Number of Hotspot Detectors Using Artificial Neural Network and Genetic Algorithm to Estimate Material Levels Inside a Silo

To realize efficient operation of a silo, level management of internal storage is crucial. In this study, to address the existing measurement limitations, a silo hotspot detector, which is typically utilized for internal silo temperature monitoring, was employed. The internal temperature data measured using the hotspot detectors were used to train an artificial neural network (ANN) algorithm to predict the level of the internal storage of the silo. The prediction accuracy was evaluated by comparing the predicted data with ground truth data. We combined the ANN model with the genetic algorithm (GA) to improve the prediction accuracy and establish efficient sensor installation positions and number to proceed with optimization. Simulation results demonstrated that the best predictive performance (up to 97% accuracy) was achieved when the ANN structure was 9-19-19-1. Furthermore, the numbers of efficient sensors and sensors positions determined using the proposed ANN-GA technique were reduced from seven to five or four, thereby ensuring economic feasibility.

Multiwalled Carbon Nanotube-N-Doped Graphene/Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) Nanohybrid for Electrochemical Application in Intelligent Sensors and Supercapacitors

In this study, we reported the preparation of a conducting polymeric/inorganic nanohybrid consisting of multiwalled carbon nanotubes (MWCNT), N-doped graphene (NGr), and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), and its electrochemical application in intelligent sensors and supercapacitors. The multilayer thin film of the PEDOT:PSS-supported MWCNT-NGr nanohybrid was prepared by a facile layer-by-layer assembly strategy. The obtained conducting polymeric/inorganic nanohybrid modified electrode displayed superior electron transfer ability and a high specific surface area, which was used for electrochemical applications in intelligent sensors and supercapacitors. Remarkably, the fabricated amaranth sensor exhibited a broad linear range of 0.05-10 μM with a limit of detection of 0.015 μM under the optimal conditions. With the help of the response surface methodology, multivariate optimization was used as a substitute for the traditional single variable optimization to reflect the complete real effects of multivariate optimization in a sensing platform. Machine learning implemented by hybrid genetic algorithm-artificial neural network was used as an intelligent analysis model to replace the traditional regression analysis model for realizing intelligent analysis and output of sensing system. The MWCNT-NGr/PEDOT:PSS modified electrode exhibited a considerable specific capacitance of 6.5 mF cm^-2 at a current density of 2.0 mA cm^-2. The proposed results provided a new thought for a nanosensing platform equipped with a supercapacitor as a self-powered electrochemical energy storage system and machine learning as an intelligent analysis and output system in the near future.

Optimisation of broiler chicken responses from 0 to 7 d of age to dietary leucine, isoleucine and valine using Taguchi and mathematical methods

Three experiments were conducted to evaluate the applicability of the Taguchi method (TM) and optimisation algorithms to optimise the branch chain amino acids (BCAA) requirements in 0 to 7 d broiler chicks. In the first experiment, the standardised digestible (SID) amino acids and apparent metabolisable energy (AME) values of maize, wheat and soya bean meal were evaluated. In the second experiment, three factors including leucine (Leu), isoleucine (Ile) and valine (Val), each at 4 levels, were selected, and an orthogonal array layout of L16 (4(3)) using TM was performed. After data collection, optimisation of average daily gain (ADG) and feed conversion ratio (FCR) were obtained using TM. The multiobjective genetic algorithm (MOGA) and random search algorithm (RSA) were also applied to predict the optimal combination of BCAA for broiler performance. In the third experiment, a growth study was conducted to evaluate the applicability of obtained optimum BCAA requirements data by TM, MOGA and RSA, and results were compared with those of birds fed with a diet formulated according to Ross 308 recommendations. In the second experiment, the TM resulted in 13.45 g/kg SID Leu, 8.5 g/kg SID Ile and 10.45 g/kg SID Val as optimum level for maximum ADG (21.57 g/bird/d) and minimum FCR (1.11 g feed/g gain) in 0- to 7-d-old broiler chickens. MOGA predicted the following combinations: SID Leu = 14.8, SID Ile = 9.1 and SID Val = 10.3 for maximum ADG (22.05) and minimum FCR (1.11). The optimisation using RSA predicted Leu = 16.0, Ile = 9.5 and Val = 10.2 for maximum ADG (22.67), and Leu = 15.5, Ile = 9.0 and Val = 10.4 to achieve minimum FCR (1.08). The validation experiment confirmed that TM, MOGA and RSA yielded optimum determination of dietary amino acid requirements and improved ADG and FCR as compared to Aviagen recommendations. However, based on the live animal validation trial, MOGA and RSA overpredicted the optimum requirement as compared to TM. In general, the results of these studies showed that the TM may be used to optimise nutrient requirements for poultry.