Modeling and optimization of pectinase-assisted low-temperature extraction of cashew apple juice using artificial neural network coupled with genetic algorithm

The plastic waste utilization can be addressed toward different valuable products. A promising technology for the utilization is by converting it to fuels. Simultaneous modeling and optimization representing effect of reactor temperature, catalyst calcinations temperature, and plastic/catalyst weight ratio toward performance of liquid fuel production was studied over modified catalyst waste. The optimization was performed to find optimal operating conditions (reactor temperature, catalyst calcination temperature, and plastic/catalyst weight ratio) that maximize the liquid fuel product. A Hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) method was used for the modeling and optimization, respectively. The variable interaction between the reactor temperature, catalyst calcination temperature, as well as plastic/catalyst ratio is presented in surface plots. From the GC-MS characterization, the liquid fuels product was mainly composed of C4 to C13 hydrocarbons.KONVERSI LIMBAH PLASTIK MENJADI BAHAN BAKAR CAIR DENGAN METODE PERENGKAHAN KATALITIK MENGGUNAKAN KATALIS BEKAS YANG TERMODIFIKASI: PEMODELAN DAN OPTIMASI MENGGUNAKAN GABUNGAN METODE ARTIFICIAL NEURAL NETWORK DAN GENETIC ALGORITHM. Pemanfaatan limbah plastik dapat dilakukan untuk menghasilkan produk yang lebih bernilai tinggi. Salah satu teknologi yang menjanjikan adalah dengan mengkonversikannya menjadi bahan bakar. Permodelan, simulasi dan optimisasi simultan yang menggambarkan efek dari suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis terhadap kinerja produksi bahan bakar cair telah dipelajari menggunakan katalis bekas termodifikasi Optimisasi ini ditujukan untuk mencari kondisi operasi optimum (suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis) yang memaksimalkan produk bahan bakar cair. Metode Hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) telah digunakan untuk permodelan dan optimisasi simultan tersebut. Inetraksi antar variabel suhu reaktor, suhu kalsinasi katalis, dan rasio berat plastik/katalis digambarkan dalam bentuk plot surface. Berdasarkan karakterisasi GC-MS, produk bahan bakar yang diperoleh terdiri dari komponen-komponen hidrokarbon C4-C13.

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Photocatalytic ozonation of ciprofloxacin from aqueous solution using TiO 2 /MMT nanocomposite: Nonlinear modeling and optimization of the process via artificial neural network integrated genetic algorithm

Process Safety and Environmental Protection ◽

10.1016/j.psep.2018.03.013 ◽

2018 ◽

Vol 116 ◽

pp. 365-376 ◽

Cited By ~ 28

Author(s):

Aydin Hassani ◽

Alireza Khataee ◽

Mehrangiz Fathinia ◽

Semra Karaca

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Aqueous Solution ◽

Artificial Neural Network ◽

Nonlinear Modeling ◽

Modeling And Optimization ◽

Photocatalytic Ozonation ◽

Artificial Neural

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Comparison of Artificial Neural Network and Response Surface Methodology Performance on Fermentation Parameters Optimization of Bioconversion of Cashew Apple Juice to Gluconic Acid

International Journal of Food Engineering ◽

10.1515/ijfe-2015-0072 ◽

2015 ◽

Vol 11 (3) ◽

pp. 393-403 ◽

Cited By ~ 4

Author(s):

Omotola B. Osunkanmibi ◽

Temitayo O. Owolabi ◽

Eriola Betiku

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Gluconic Acid ◽

Apple Juice ◽

Inoculum Size ◽

Ann Model ◽

Cashew Apple Juice ◽

Cashew Apple ◽

Rsm Model

Abstract The study examined the impact and interactions of cashew apple juice (CAJ) concentration, pH, NaNO3 concentration, inoculum size and time on gluconic acid (GA) production in a central composite design (CCD). The fermentation process and parameters involved were modeled and optimized using artificial neural network (ANN) and response surface methodology (RSM). The ANN model established the optimum levels as CAJ of 250 g/l, pH of 4.21, NaNO3 of 1.51 g/l, inoculum size of 2.87% volume and time of 24.41 h with an actual GA of 249.99 g/l. The optimum levels predicted by RSM model for the five independent variables were CAJ of 249 g/l, pH of 4.6, NaNO3 of 2.29 g/l, inoculum size of 3.95% volume, and time of 38.9 h with an actual GA of 246.34 g/l. The ANN model was superior to the RSM model in predicting GA production. The study demonstrated that CAJ could serve as the sole carbon source for GA production.

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