Perspective on multiscale methodology for product design and engineering

An Overview of the Application of Harmony Search for Chemical Engineering Optimization

Harmony search algorithm and its variants have been used in several applications in medicine, telecommunications, computer science, and engineering. This article reviews the global and multi-objective optimization for chemical engineering using harmony search. The main features of the HS method and several of its popular variants and hybrid versions including their relevant algorithm characteristics are described and discussed. A variety of global and multi-objective optimization problems from chemical engineering and their resolution using HS-based methods are also included. These problems involve thermodynamic calculations (phase stability analysis, phase equilibrium calculations, parameter estimation, and azeotrope calculation), heat exchanger design, distillation simulation, life cycle analysis, and water distribution systems, among others. Remarks on future developments of HS and its related algorithms for global and multi-objective optimization in chemical engineering are also provided in this review. HS is a reliable and promising stochastic optimizer to resolve challenging global and multi-objective optimization problems for process systems engineering.

Deriving force fields with a multiscale approach:from ab initio calculations to molecular-based equations of state

Using theoretical and computational tools for predicting thermophysical properties of fluid systems and the soft matter has always been of interest to the physical, chemical, and engineering sciences. And certainly, the ultimate goal is to be able to compute these macroscopic properties from first principle calculations beginning with the very atomic constitution of matter. In this work, Mie potential parameters were obtained through dimer interaction energy curves derived from ab initio calculations to represent methane and methane-substituted molecules in a spherical 1-site coarse-grained model. Bottom-up-based Mie potential parameters of this work were compared to top-down-based ones from the statistical associating fluid theory (SAFT) models for the calculation of thermodynamic properties and critical point by molecular dynamics simulations and SAFT-VR Mie equation of state. Results demonstrated that bottom-up-based Mie potential parameters when averaging the Mie potential parameters of a representative population of conformers provide values close to the top-down-based ones from SAFT models and predict well properties of tetrahedral molecules. This shows the level of consistency embedded in the SAFT-VR Mie family of models and confers a status of a purely predictive equation of state for SAFT-VR Mie when a reasonable model is considered to represent a molecule of interest.

Mesoscopic Simulation for the Effect of Cross-Linking Reactions on the Drug Diffusion Properties in Microneedles

The drug diffusion issue in microneedles is the focus of its medical application. It will not only affect the distribution of drugs in the needle body but will also have an impact on the drug release performance of the microneedle. The utilization of cross-linked polymer materials to obtain the drug diffusion control has been experimentally verified as a feasible method. However, the mechanism research on the molecular level is still incomplete. In this study, the dissipative particle dynamics (DPD) simulation has been applied to study the effect of the cross-linking reaction on drug diffusion in hyaluronic acid microneedles. We have discovered that when the cross-linking degree reaches 90%, the diffusion coefficient of the drug is 6.45 times lower than that of the uncross-linked system. The main reason for the decline in drug diffusion ability is that the cross-linking reaction varies the conformation of the polymer. The amplification in the cross-linking degree makes the polymer coils more compact and approach each other, finally forming a continuously distributed cross-linked network, which reduces its degradation rate in the body. Simultaneously, these cross-linked networks can also hinder the interaction of soluble drugs with water, thereby preventing the premature release of drugs. The simulation results are consistent with the data collected in the previous microneedle experiment. This work will be an extension of DPD simulation in the application of biological materials.

Multiscale and Multi-Granularity Process Analytics: A Review

As Industry 4.0 makes its course into the Chemical Processing Industry (CPI), new challenges emerge that require an adaptation of the Process Analytics toolkit. In particular, two recurring classes of problems arise, motivated by the growing complexity of systems on one hand, and increasing data throughput (i.e., the product of two well-known “V’s” from Big Data: Volume × Velocity) on the other. More specifically, as enabling IT technologies (IoT, smart sensors, etc.) enlarge the focus of analysis from the unit level to the entire plant or even to the supply chain level, the existence of relevant dynamics at multiple scales becomes a common pattern; therefore, multiscale methods are called for and must be applied in order to avoid biased analysis towards a certain scale, compromising the benefits from the balanced exploitation of the information content at all scales. Also, these same enabling technologies currently collect large volumes of data at high-sampling rates, creating a flood of digital information that needs to be properly handled; optimal data aggregation provides an efficient solution to this challenge, leading to the emergence of multi-granularity frameworks. In this article, an overview is presented on multiscale and multi-granularity methods that are likely to play an important role in the future of Process Analytics with respect to several common activities, such as data integration/fusion, de-noising, process monitoring and predictive modelling, among others.