Machine learning modeling and additive explanation techniques for glutathione production from multiple experimental growth conditions of Saccharomyces cerevisiae.

Glutathione (GSH) production is of great industrial interest due to its essential properties. This study aimed to use machine learning (ML) methods to model GSHproduction under different growth conditions of Saccharomyces cerevisiae, namely cultivation time, culture volume, pressure, and magnetic field application. Different ML and regression models were evaluated for their statistics to select the most robust model.

Adsorption of atrazine and 2,4-D pesticides on alternative biochars from cedar bark sawdust (Cedrella fissilis).

Bark residues of the forest species Cedrela fissilis were physically and chemically modified with zinc chloride (ZnCl) as an activating agent. The two modified materials were analyzed as adsorbents in removing atrazine and 2,4-D herbicides from effluents. Firstly, the precursor material and the modified ones were characterized by different techniques to identify the structural changes that occurred in the surfaces.

Optimization-based artificial neural networks to fit the isotherm models parameters of aqueous-phase adsorption systems.

An artificial neural network (ANN) hybrid structure was proposed that, unlike the standard ANN structure optimization, allows the fit of several adsorption curves simultaneously by indirectly minimizing the real output error. To model a case study of 3-aminophenol adsorption phenomena onto avocado seed activated carbon, a hybrid ANN was applied to fit the parameters of the Langmuir and Sips isotherm models. Network weights and biases were optimized with two different methods: particle swarm optimization (PSO) and genetic algorithm (GA), due to their good convergence in large-scale problems.

An approach to assess and identify polymers in the health-care waste of a Brazilian university hospital.

This work presents the health-care waste (HCW) management and an approach to assess and identify polymers in a General Surgery Unit - Internment Service (GSU) of a Brazilian university hospital, to estimate the main polymers presenting in medical devices that are consumed during a year, discarded either as infecting (Group A) or as scarifying residue (Group E). Among the waste produced from the medical devices, 3.14 ton (98.

Analysis of indium (III) adsorption from leachates of LCD screens using artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANIFS).

Ten different adsorbent materials were tested to adsorb indium (III) from leachates of LCD screens, aiming to concentrate this valuable material. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANIFS) were applied to analyze the indium (III) adsorption. The input variables for the network models were: specific surface area, point of zero charge, adsorbent dosage and contact time.

Identifyability measures to select the parameters to be estimated in a solid-state fermentation distributed parameter model.

Process modeling can lead to of advantages such as helping in process control, reducing process costs and product quality improvement. This work proposes a solid-state fermentation distributed parameter model composed by seven differential equations with seventeen parameters to represent the process. Also, parameters estimation with a parameters identifyability analysis (PIA) is performed to build an accurate model with optimum parameters.

Modeling the microbial growth and temperature profile in a fixed-bed bioreactor.

Aiming to scale up and apply control and optimization strategies, currently is required the development of accurate plant models to forecast the process nonlinear dynamics. In this work, a mathematical model to predict the growth of the Kluyveromyces marxianus and temperature profile in a fixed-bed bioreactor for solid-state fermentation using sugarcane bagasse as substrate was built up. A parameter estimation technique was performed to fit the mathematical model to the experimental data.

Optimization of enzymatic hydrolysis of cassava to obtain fermentable sugars.

This work evaluates the enzymatic hydrolysis of starch from cassava using pectinase, α-amylase, and amyloglucosidase. A central composite rotational design (CCRD) was carried out to evaluate the effects of amyloglucosidase, pectinase, reaction time, and solid to liquid ratio. All the experiments were carried out in a bioreactor with working volume of 2 L.