Search

Lignocellulosic materials can come from a variety of sources, including agro-industrial residues, and can serve as raw material to produce various chemical compounds. There is an appeal to produce bioplastics from lignocellulosic materials because they have similar applications and are biodegradable. For instance, the use of poly-lactic acid (PLA) has been encouraged to meet the demand for alternatives that reduce the environmental impacts caused by plastic waste. Before obtaining bioplastics, the lignocellulosic material (sugarcane bagasse) undergoes a pre-treatment process to make its constituents available (cellulose, hemicellulose, lignin and others). Thus, they can be fermented by producing microorganism. The supercritical technologies (such as H2O) in the pre-treatment and conversion of cellulose represents an alternative to conventional methods as they generate less waste. Given this scenario, this work aimed to study the production of PLA, derived from sugarcane bagasse constituents and fermentation by Bacillus coagulans to produce lactic acid (LA). Thus, the steps of supercritical pre-treatment, fermentation for LA production and polymerization (PLA production) in the Aspen Plus simulator were simulated. The results showed that it was possible to simulate all steps, a compatible route, being able to aggregate these processes in an ethanol production biorefinery. In this way, a strategy can be traced to conduct future simulations involving new scenarios.

There is an increasing interest in the secondary sources of metals in order to compensate their potential supply deficit. Copper is the main component of printed circuit boards (PCBs) which is the main part of an electronic device. The manufacture of PCBs often releases huge amount of sludge, which contains copper. The process of copper leaching by hydrochloric acid solution was studied using experimental design methods using the Solver of Excel software. The parameters were simulated and simulated to evaluate the effect of the acid concentration and oxygen flow. The results obtained by prediction were compared with real experimental results. The intelligence modeling was confirmed by analysis of variance (ANOVA) and the model was able to predict the response. Through the investigation of response surface and contour curves, it was identified the conditions optimized for the process.

In the chemical industry is important to control the process in order to guarantee the quality and repeatability of the final product. Using sensors in the industrial plant allows a large volume of data to be captured regarding the process. These data can be used for modelling to better understanding and predict the properties of the product in the process. In this work, two types of Artificial Neural Networks (ANN) and the hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS) were used to predict the density of polystyrene along the styrene polymerization process. The dataset used was extracted from the batch of polymerization reactions performed in open-loop, manual control and closed-loop and monitored in each 5 seconds. The Feedforward and Elman ANN has coefficient of correlation (R) equal 94.2%. However, the best topology obtained to Feedforward ANN presents 2 hidden layers and error index RMSE (Root Mean Squared Error) equal to 2.69x10-2. The Elman ANN presents only 1 hidden layer and RMSE of 3.39x10-2. The ANFIS model, in turn, presented R equal to 91% and RMSE of 0.2123. Therefore, ANFIS model did not prove to be the most adequate for the prediction of the polystyrene density in the studied process. Polymerization process pose significant challenges to the industrial community as it is difficult to control with high nonlinearity behaviour and fast dynamic response. The monitoring and control of polymer processes guarantee to the final product the qualities required by the market. Muhammad and Aziz (2017), for instance, studied the production of low density polyethylene (LDPE) and presented a review of the control strategies developed for the LDPE process. The strategies presented were developed in tubular and autoclave reactors and highlights the importance of nonlinear control in polymerization process. The use of sensors to monitor production allows a large volume of process data to be collected. Therefore, it is necessary to construct mathematical models of prediction to interpret and correlate significant patterns, indispensable to assist in the management of decisions and risk analysis. The development of phenomenological models for polymerization is complex and requires deep knowledge about the processes involved in each step. Modelling using artificial intelligence is a strategy that can provide valuable information about the process and allows the construction of an intelligent model capable of predicting process response based on parameters provided. ANN and ANFIS are artificial intelligence tools that can be used to build predictive models. In supervised learning, the data are presented to the network and its main objective is to provide a model that correctly correlates the pairs inputs - outputs of the problems. The use of ANN and ANFIS to predict the density of the polystyrene produced in the process becomes attractive since it is a type of non-linear modelling. Jumari and Mohd-Yusof (2017) presents models to measure melt flow index (MFI) in industrial polypropylene loop reactors using first principle (FP) model and ANN model. The authors state that the prediction of the ANN model is more accurate compare to the MFI calculated by the FP model. Furthermore, the CPU time recorded that ANN model is much faster than FP model. This work aims to develop direct and recursive ANN and ANFIS models from a set of experimental data from a controlled styrene polymerization plant capable of predicting the density of the product satisfactorily.

Long-life packages are composed by two main materials: paper and low-density polyethylene (LDPE) with aluminium composite. Usually, the packages are disposed in landfills, generating an environmental impact due to its increasing consume. Recycling could be a solution if the recycled and raw materials used in the production have the same properties, being necessary to perform tests to identify the similarities and differences of each material. This study aims the study of thermal decomposition behaviour and kinetic parameters estimation of raw and recycled long-life packages low-density polyethylene using thermogravimetric analysis (TGA) under inert condition in temperature range of 25 – 700 °C. Classical methods, for example Kissinger and Ozawa, depend on many heating rates to determine kinetic parameters. Stochastic global search algorithms, such as Genetic Algorithm (GA), have great potential in parameter estimation using only one heating rate. The software Matlab 2018b was used to calculate all the models and the efficiency was evaluated using the determination coefficient (R²). The results showed a difference in the reaction order but similar pre-exponential and activation energy values.

The development of more economically and energy-efficient processes for the sustainable production of fuels and chemicals is becoming increasingly necessary. In this context, it is relevant to understand the behaviour of thermal degradation of different biomasses in oxygen free atmosphere to investigate the breakdown of polymer chains, which can be converted into new products. Good effects on the acceleration of degradation of organic molecules can be achieved with the use of catalysts in these breaking processes, thus increasing the yield of bio-oil production. In this work, fiber of crushed and sifted green coconut shell, submitted to thermogravimetry analysis (TG), was used as biomass. Some types of a catalyst were incorporated into the biomass, based on cobalt ferrite, Fe2CoO4. The design of a biomass pyrolysis reactor requires knowledge of kinetic parameters, activation energy (Ea), pre-exponential factor (k0) and order of reaction (n). In this context, the present study aimed to perform the thermogravimetric (TG) analysis in an inert atmosphere of pure nitrogen, and to determine the kinetic parameters involved in the lignin pyrolysis process, to assist in the design of biomass conversion reactors. This work presents a solution to obtain the kinetic parameters for thermogravimetric reaction of lignin breaking present in the biomass of the green coconut. Two computational methods were used: genetic algorithm and particle swarm optimization. The results obtained for activation energy, pre-exponential factor and order reaction are in the range of values found in the literature.

The use of the biodigestion is considered promising for the energetic valorization of agriculture biomass such as swine farm sewage and lignocelulosic residues. The understanding of biodigesters operation and the control of their main operational variables are of great importance to improve the performance of anaerobic digestion process in order to increase biogas production. In this context, mathematical modelling can be used as a tool to increase process efficiency. This work presents the development of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) to predict volume of biogas. The variables from process were temperature (ºC), pH, FOS/TAC ratio and type of biodigesters. A database was constructed with the information of the experiments, dividing them into groups of training (67 %) and test (33 %). The models were obtained using MATLAB R2018b toolbox. In the developed neural models, the data obtained from process were used as neurons in the input layer and the volume of biogas was used as the only neuron in the output layer. The performance of the neural models was evaluated by determination coefficient (R²) and error index (RMSE). The model developed from ANN and ANFIS modelling were satisfactory, showing the R² value giving the system’s complexity. In addition, the RMSE values of both models were close to each other, showing agreement of the methods used.