Pyrolysis of Mixed Plastic Waste: II. Artificial Neural Networks Prediction and Sensitivity Analysis  
[PERSON]  
The Global Generation rate of municipal solid waste (MSW) has reached 2.01 Bt/year and it is expected to reach 2.59 Bt/year by 2030 [1]. In Saudi Arabia, 5.2% of the MSW is plastic waste [2]; 15.3 Mt/year of MSW was generated in 2014 and the rate is expected to reach 30 Mt/year by 2033 [3]. Due to the current challenges of plastic recycling, most of the plastic waste is either incinerated or disposed of in landfills, and thus produced harmful products and emissions causing critical environmental issues [4]. The pyrolysis of plastic waste has been considered as a promising alternative treatment option where oil and gas products, with high caloric values, can be used as fuel for some engines. [PERSON] et al. [5] investigated the products (oil and/or gas) of the pyrolysis of low-density polyethylene (LDPE), high-density polyethylene (HDPE), polypropylene (PP), and polystyrene (PS). The effect of catalysts on the pyrolysis products was studied using lignite and zeolite catalysts. It was reported that the caloric values of the produced oil ranged between 40.17 and 45.35 MJ/kg, and thus it was recommended as an alternative fuel for diesel engines. In addition, the produced oil had a lower sulfur content that was found to be below the acceptable limit. Furthermore, the produced gas was rich in hydrocarbons and has caloric values ranging between 73.42 and 121.18 MJ/kg. In addition, [PERSON] et al. [6] explored the types of oil and gas produced from the pyrolysis of PP, polyethylene (HDPE and LDPE), and PS. Two mesoporous silica materials (MCM-41 and SBA-15) were used. A positive effect of the catalyst on the caloric values of the produced oil and gas was reported. Also, it was claimed that the produced oil and gas are much cheaper than those available on the market. In Saudi Arabia, plastic wastes mainly comprise LDPE, HDPE, PS, polyvinyl chloride (PVC), and PP [7]. Due to its powerful performance to predict efficiently different experimental data, artificial neural network (ANN) modeling has attracted the attention of many researchers to be used for a variety of engineering applications. For example, ANN models can be used to predict the thermogravimetric analysis (TGA) data of the materials' thermal decomposition (pyrolysis). In 2004, [PERSON] et al. [8] developed, for the first time, an ANN model to predict the kinetic parameters of the pyrolysis of some materials using TGA-experimental data at different heating rates. [PERSON] et al. [9] predicted the TGA data of the pyrolysis of carbon-reinforced fiber composites and carbon fibers using ANN. Two input parameters (temperature and heating rate) and an output layer (mass retained) were used. The optimum ANN topology was reported as 2-21-21-1. [PERSON] et al. [10] used ANN to predict the thermal stability and crystallinity of the mixture of polyethylene oxide and clay using experimental data obtained by differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and TGA. For TGA experiments, an ANN model, with a feed-forward back-propagation, Levenberg-Marquardt (LM) algorithm, and TANSIG-LOGSIG-purelin transfer function, for the first and second hidden layers, was structured. Temperature and clay weight percent were the input parameters to predict weight loss percent. Excellent performance, with a regression coefficient of 0.99994, was reported. [PERSON] et al. [11] developed an ANN model to predict the weight loss percent during the thermal decomposition of some blends with different compositions using non-isothermal TGA data covering a wide temperature range (298-1273 K). Three input parameters, namely temperature, blend ratio, and heating rate, were used as the input variables. Good performance of the proposed model was reported. [PERSON] et al. [12] investigated the weight loss of a refuse-derived fuel during its pyrolysis process by developing an ANN model using TGA data. Two input parameters (heating rate, and temperature) were used.