Dersleri yüzünden oldukça stresli bir ruh haline sikiş hikayeleri bürünüp özel matematik dersinden önce rahatlayabilmek için amatör pornolar kendisini yatak odasına kapatan genç adam telefonundan porno resimleri açtığı porno filmini keyifle seyir ederek yatağını mobil porno okşar ruh dinlendirici olduğunu iddia ettikleri özel sex resim bir masaj salonunda çalışan genç masör hem sağlık hem de huzur sikiş için gelip masaj yaptıracak olan kadını gördüğünde porn nutku tutulur tüm gün boyu seksi lezbiyenleri sikiş dikizleyerek onları en savunmasız anlarında fotoğraflayan azılı erkek lavaboya geçerek fotoğraflara bakıp koca yarağını keyifle okşamaya başlar
GET THE APP
Application Of Genetic Algorithm And Artificial Neural Networks In Bioremediation Process | 4838
Journal of Biotechnology & Biomaterials
Like us on:
Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.
Development of an automated effluent treatment plant is very difficult as the parameters of an industrial effluent change
continuously. Hence, a computer-simulated model is required for predicting the relationship between input and output
parameters. Experiments were conducted to study the efficiency of adsorbent prepared from Acacia Arabica fruits for removal
of Methylene Blue (MB) dye. The adsorbed dye on green carbon was characterized by Fourier transform-infrared spectroscopy
(FTIR) and Scanning electron microscopy (SEM). RSM was used to design the experimental runs. An artificial neural network
model was developed and optimized for the prediction of percentage removal of dye from the effluent. The network was trained
using the experimental data obtained at different process parameters such as temperature, initial pH, contact time, adsorbent
dosage and initial dye concentration of the solution. Different algorithms and transfer functions for hidden layer have been tested
to find the most suitable and reliable network. The prediction efficiency of this ANN model was tested and it was found that
prediction was good.
The ANN model developed was optimized using genetic algorithm (GA). This experiment revealed that the adsorbent
exhibited high adsorption capacities. The kinetic data obtained was analyzed using pseudo-first order, pseudo-second order and
intra particle diffusion models. Thermodynamic studies were also carried out. The adsorption was efficient and both Langmuir
and Freundlich isotherm models showed good fit into the experimental data. From these studies, it may be concluded that green
carbon adsorbent prepared is efficient and economical for Methylene blue removal from aqueous solutions.
Narayana Saibaba K.V has completed his B.Tech in Chemical Engineering with Biotechnology as specialization from Andhra University and Masters
in Chemical Engineering with Petroleum Refining Engineering as elective from Andhra University. He also completed MBA with dual specialization
in HRM and Finance. He is currently pursuing his Ph.D under the guidance of Prof. P. King. He has published more than 15 papers in international
journals of repute. His papers also published in the CHEMCON (top workshop for chemical engineers). Prof. P. King published more than 100 papers
in reputed journals and serving as editorial board member of repute journals.
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals