Application of response surface methodology for modeling of ball mills in copper sulphide ore grinding

Optimizing Anti-inflammatory Activities of Arabica Coffee Ground (Coffea arabica L.) Nanoparticle Gel

Background: Arabica coffee ground nanoparticles gel is known to possess good physicochemical properties and high phenolic contents in which the phenolics are famous for their anti-inflammatory activities. Objectives: : This study aimed to investigate the anti-inflammatory activities of the arabica coffee ground nanoparticles gel on carrageenan-induced male mice for 6 h. The positive control was commercial medicine (Voltaren gel), and the control negative was base gel. Methods: The inflammatory inhibition of arabica coffee ground nanoparticles gel is determined by measuring the edema volume of the carrageenan-induced mice foot using a plethysmometer. Anti-inflammatory activities of arabica coffee ground nanoparticles gel were optimized using Box-Behnken Design of the response surface methodology. Results: The anti-inflammatory activities or inflammatory inhibition of arabica coffee ground nanoparticles gel was determined by measuring the edema volume of the carrageenan-induced mice foot. The increase of edema volume of the mice foot from the first to 180th min was caused by the release of inflammation mediator such as histamine, prostaglandins, bradykinins, and serotonin on the tissue; meanwhile, at the 240th min, the edema of the mice foot experienced a decrease due to the inhibition of prostaglandins synthesis to the tissue. The obtained results indicated that the arabica coffee ground nanoparticles gel has average value of anti-inflammatory activities of 27.75%. The obtained optimum formulation is 0.500% Carbopol 940, 0.400% triethanolamine, and 2.313% nanoparticle, giving 29.360% and 29.670% for the predicted and experimental inflammatory inhibition values, with 0.812 desirability value, respectively. Conclusions: The Box-Behnken design can be used to determine the optimal gel formulation of arabica coffee grounds for inflammatory inhibition.

A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis

In the manufacturing processes, process optimization tasks, to optimize their product quality, can be performed through the following procedures. First, process models mimicking functional relationships between quality characteristics and controllable factors are constructed. Next, based on these models, objective functions formulating process optimization problems are defined. Finally, optimization algorithms are applied for finding solutions for these functions. It is important to note that different solutions can be found whenever these algorithms are independently executed if a unique solution does not exist; this may cause confusion for process operators and engineers. This paper proposes a confidence interval (CI)-based process optimization method using second-order polynomial regression analysis. This method evaluates the quality of the different solutions in terms of the lengths of their CIs; these CIs enclose the outputs of the regression models for these solutions. As the CIs become narrower, the uncertainty about the solutions decreases (i.e., they become statistically significant). In the proposed method, after sorting the different solutions in ascending order, according to the lengths, the first few solutions are selected and recommended for the users. To verify the performance, the method is applied to a process dataset, gathered from a ball mill, used to grind ceramic powders and mix these powders with solvents and some additives. Simulation results show that this method can provide good solutions from a statistical perspective; among the provided solutions, the users are able to flexibly choose and use proper solutions fulfilling key requirements for target processes.

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

Multiphase systems are important in minerals processing, and usually include solid–solid and solid–fluid systems, such as in wet grinding, flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization in multiphase systems, are analyzed. Different scales of size and time are included, and therefore, the analysis includes modeling at the molecular level (molecular dynamic modeling) and unit operation level (e.g., computational fluid dynamic, CFD), and the application of optimization for the design of a plant. New strategies for the modeling, design, and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The integration of different modeling techniques such as CFD with discrete element simulation (DEM) and response surface methodology (RSM) with artificial neural networks (ANN) is included. The paper finishes with tools to study the uncertainty, both epistemic and stochastic, based on uncertainty and global sensitivity analyses, which is present in all mineral processing operations. It is shown that all of these areas are very active and can help in the understanding, operation, design, and optimization of mineral processing that involves multiphase systems. Future needs, such as meso-scale modeling, are highlighted.