Predicting and interpreting oxide glass properties by machine learning using large datasets

With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly machine learning algorithms. We investigated the predictive performance of three machine learning algorithms for six different glass properties. For such, we used an extensive dataset of about 150,000 oxide glasses, which was segmented into smaller datasets for each property investigated. Using the decision tree induction, k-nearest neighbors, and random forest algorithms, selected from a previous study of six algorithms, we induced predictive models for glass transition temperature, liquidus temperature, elastic modulus, thermal expansion coefficient, refractive index, and Abbe number. Moreover, each model was induced with default and tuned hyperparameter values. We demonstrate that, apart from the elastic modulus (which had the smallest training dataset), the induced predictive models for the other five properties yield a comparable uncertainty to the usual data spread. However, for glasses with extremely low or high values of these properties, the prediction uncertainty is significantly higher. Finally, as expected, glasses containing chemical elements that are poorly represented in the training set yielded higher prediction errors. The method developed here calls attention to the success and possible pitfalls of machine learning algorithms. The analysis of the SHAP values indicated the key elements that increase or decrease the value of the modeled properties. It also estimated the maximum possible increase or decrease. Insights gained by this analysis can help empirical compositional tuning and computer-aided inverse design of glass formulations.     

Modeling of a reaction network and its optimization by genetic algorithm

Continuous endeavors are going on in many research works to find out the strategy to mathematically model and optimize complex reaction networks in order to maximize the main product and at the same time keeping the reactor dimensions within some acceptable limits. The aim of this work is to provide with a strategy for efficient modeling and optimization of reaction networks for reaction controlled processes. Genetic algorithm (GA) has been used for optimizing complex search spaces with multiple optima. Formation of styrene monomer from the ethylbenzene dehydrogenation, with several by-products in a fixed bed reactor, is taken as an example for this study. Two activation energies are found to be the best in term of maximizing styrene productivity.     

Electrical and optical properties of a polyblend electrolyte

A potassium ion conducting polyblend electrolyte based on polyvinyl pyrrolidone (PVP)+polyvinyl alcohol (PVA) complexed with KBrO₃ was prepared using solution cast technique. The electrical conductivity increased with increasing dopant concentration. Optical absorption studies were made in the wavelength range (200-600 nm) on pure (PVP+PVA) and KBrO₃ doped (PVP+PVA) films. The absorption edge was observed at 5.13 eV for undoped (PVP+PVA) while it ranged from 4.88 to 5.0 eV for differently KBrO₃-doped samples. The direct band gaps for undoped and KBrO₃ doped (PVP+PVA) films were found to be, respectively, 5.05 and 4.95, 4.86 and 4.90 eV while the indirect band gaps were 5.03 and 4.88, 4.79 and 4.83 eV, respectively. The absorption edge and the band gaps moved towards lower energies as the dopant concentration was increased up to 20 wt% of the dopant. For further increase in dopant concentration these values started increasing again. This is explained in terms of formation of charge transfer complexes between the dopant and the host matrix. The thermal properties of these films were investigated with differential scanning calorimetry (DSC). The variation in film morphology is examined by scanning electron microscopic examination.     

Optimization of short-time gasoline blending scheduling problem with a DNA based hybrid genetic algorithm

Gasoline blending is a key process in the petroleum refinery industry posed as a nonlinear optimization problem with heavily nonlinear constraints. This paper presents a DNA based hybrid genetic algorithm (DNA-HGA) to optimize such nonlinear optimization problems. In the proposed algorithm, potential solutions are represented with nucleotide bases. Based on the complementary properties of nucleotide bases, operators inspired by DNA are applied to improve the global searching ability of GA for efficiently locating the feasible domains. After the feasible region is obtained, the sequential quadratic programming (SQP) is implemented to improve the solution. The hybrid approach is tested on a set of constrained nonlinear optimization problems taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm. The recipes of a short-time gasoline blending problem are optimized by the hybrid algorithm, and the comparison results show that the profit of the products is largely improved while achieving more satisfactory quality indicators in both certainty and uncertainty environment.