Effect of Stirring Time on Sodium Silicate Synthesis From Mount Kelud Volcanic Ash

This study performed the synthesis of sodium silicate from the volcanic ash of Mount Kelud with various stirring time in the sodium silicate synthesis process. Synthesis of sodium silicate was carried out using alkaline extraction at low temperature. This method is based on the solubility of silica under alkaline conditions and is proven to be more effective than the smelting method. The dissolution of silica contained in the volcanic ash of Mount Kelud was carried out using NaOH solution to form a Na2SiO3 solution. This process also studied the effect of stirring time on the amount of dissolved silica, with the stirring time of 0.5; 1; 1.5; 2; 2.5; 3 and 4 hours. The dissolved silica levels were tested using AAS. The amount of dissolved silica increased sharply with the longer length of stirring time, however for a stirring time of more than 2.5 hours, the dissolution was slow. The study revealed that the optimal result time was obtained from the stirring time of 2.5 hours with dissolved silica content of 19.82%. Keywords: volcanic ash, silica, sodium silicate, stirring time

Reconfigurable Morphological Processor for Grayscale Image Processing

Grayscale morphology is a powerful tool in image, video, and visual applications. A reconfigurable processor is proposed for grayscale image morphological processing. The architecture of the processor is a combination of a reconfigurable grayscale processing module (RGPM) and peripheral circuits. The RGPM, which consists of four grayscale computing units, conducts grayscale morphological operations and implements related algorithms of more than 100 f/s for a 1024 × 1024 image. The periphery circuits control the entire image processing and dynamic reconfiguration process. Synthesis results show that the proposed processor can provide 43.12 GOPS and achieve 8.87 GOPS/mm2 at a 220-MHz system clock. The simulation and experimental results show that the processor is suitable for high-performance embedded systems.

Homotopy Continuation Enhanced Branch and Bound Algorithms for Process Synthesis Using Rigorous Unit Operation Models

Process synthesis using rigorous unit operation models is highly desirable to identify the most efficient pathway for sustainable production of fuels and value-added chemicals. However, it often leads to a large-scale strongly nonlinear and nonconvex mixed integer nonlinear programming (MINLP) model. In this work, we propose two robust homotopy continuation enhanced branch and bound (HCBB) algorithms (denoted as HCBB-FP and HCBB-RB) where the homotopy continuation method is employed to gradually approach the optimal solution of the NLP subproblem at a node from the solution at its parent node. A variable step length is adapted to effectively balance feasibility and computational efficiency. The computational results demonstrate that the proposed HCBB algorithms can find the same optimal solution from different initial points, while the existing MINLP algorithms fail or find much worse solutions. In addition, HCBB-RB is superior to HCBB-FP due to lower computational effort required for the same locally optimal solution.