Simulation basis for a techno-economic evaluation of chitin nanomaterials production process using Aspen Plus® software.

Process simulation is a useful tool that has been widely used to analyze, design and optimize energy balances in chemical technologies including those related to biomass processing, biorefinery processes and chemical engineering. The presented data set serves as basis for the simulation of chitin purification, nanofibers and nanocrystals production processes, considering laboratory experimental procedures described in previous experimental articles.

Comparison of nanocrystals and nanofibers produced from shrimp shell α-chitin: From energy production to material cytotoxicity and Pickering emulsion properties.

Chitin nanocrystals (ChNCs) and chitin nanofibers (ChNFs) are nanomaterials with great innovative potential for sustainable applications in academic and industrial fields. The research related to their isolation and production, characterization, and utilization is still new. The aim of this study is to investigate the effects of the production process on the morphology and properties of ChNFs and ChNCs produced from the same source of chitin. ChNCs were prepared by acid hydrolysis of commercial shrimp shell α-chitin, and ChNFs were prepared by mechanical defibrillation using closed loop supermass colloidal grinding. Differences in their shape, size, and crystallinity were observed. ChNFs were observed to have higher aspect ratio, higher viscosity, and better thermal stability than ChNCs. Although the ChNC casting film had a higher degree of transparency, it had lower mechanical properties than ChNF film. In addition, the capacities of each nanomaterial for producing Pickering emulsions were comparatively investigated. ChNFs showed better oil-in-water emulsion stabilization ability than ChNCs at the same concentrations. In vitro cytotoxicity assays using two epithelial-like cell lines and two fibroblast-like cell lines demonstrated that both nanomaterials were non-toxic. Finally, we evaluated the economics of production using process engineering simulation to assess the energy and chemical consumption for each process of production of these nanomaterials.

Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks.

Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic for carving such complex problem. In this paper, we used a multilayer perceptron neural network to forecast the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 µm (PM10) in Algiers, Algeria. The data for training and testing the network are based on the data sampled from 2002 to 2006 collected by SAMASAFIA network center at El Hamma station. The meteorological data, air temperature, relative humidity, and wind speed, are used as inputs network parameters in the formation of model. The training patterns used correspond to 41 days data. The performance of the developed models was evaluated on the basis index of agreement and other statistical parameters. It was seen that the overall performance of model with 15 neurons is better than the ones with 5 and 10 neurons. The results of multilayer network with as few as one hidden layer and 15 neurons were quite reasonable than the ones with 5 and 10 neurons. Finally, an error around 9% has been reached.