Dynamic Optimization and Non-linear Model Predictive Control to Achieve Targeted Particle Morphologies

Using unsupervised learning to classify inlet water for more stable design of water reuse in industrial parks

The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of 'water quality distance' was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies. Of the six cases, three were employed to illustrate the feasibility of the unsupervised learning clustering algorithm. The results indicated that the clustering algorithm exhibited greater stability and excellence compared to both artificial clustering and ChatGPT-based clustering. The remaining three cases were utilized to showcase the reliability of the three clustering algorithms. The findings revealed that the AGNES algorithm demonstrated superior potential application ability. The average purity in six cases of K-means, DBSCAN, and AGNES were 0.947, 0.852, and 0.955, respectively.

Raman Spectroscopy of Emulsions and Emulsion Chemistry

Emulsions are dispersed systems widely used in various industries. In recent years, Raman spectroscopy (RS), as a spectroscopic technique, has gained much attention for measuring and monitoring emulsions. In this review, we explore the use of RS on emulsion structures and emulsification, important reactions that use emulsions such as emulsion polymerization, catalysis and cascading reactions, as well as various applications of emulsions. We explore how RS is used in emulsions, reactions and applications. RS is a powerful and versatile tool for studying emulsions, but there are also challenges in using RS to monitor emulsion processes, especially if they are rapid or volatile. We also explore these challenges and difficulties, as well as possible designs that can be used to overcome them.

Polymer Colloids: Current Challenges, Emerging Applications, and New Developments

Polymer colloids are complex materials that have the potential to be used in a vast array of applications. One of the main reasons for their continued growth in commercial use is the water-based emulsion polymerization process through which they are generally synthesized. This technique is not only highly efficient from an industrial point of view but also extremely versatile and permits the large-scale production of colloidal particles with controllable properties. In this perspective, we seek to highlight the central challenges in the synthesis and use of polymer colloids, with respect to both existing and emerging applications. We first address the challenges in the current production and application of polymer colloids, with a particular focus on the transition toward sustainable feedstocks and reduced environmental impact in their primary commercial applications. Later, we highlight the features that allow novel polymer colloids to be designed and applied in emerging application areas. Finally, we present recent approaches that have used the unique colloidal nature in unconventional processing techniques.

Coupled stochastic simulation of the chain length and particle size distribution in miniemulsion radical copolymerization of styrene and N-vinylcaprolactam

Kinetic Monte Carlo modeling is applied for the coupled simulation of the chain length and particle size distribution in isothermal batch miniemulsion copolymerization of styrene and N-vinylcaprolactam.