Chemical machinery and equipment and experimental instruments
| Mechanical equipment type | Uses | Examples |
| Reaction Equipment | Equipment used to carry out chemical reactions, involving temperature, pressure control, etc. |
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| Separation Equipment | Used to separate a mixture into its components by physical or chemical means. |
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| Heat Exchange Equipment | Heating or cooling materials by heat transfer. |
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| Transfer Equipment | Used to transport liquids, gases, solids or mixtures. |
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| Storage Equipment | Used for storage of gaseous or liquid raw materials and products. |
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| Measurement & Analysis Instruments | Equipment for measuring and analyzing the physical and chemical properties, content, and structure of substances. |
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| References | Authors | Abstract | DOI |
| Nanomaterials and equipment for chemical–mechanical polishing of single-crystal sapphire wafers | Shaoping Li, Jieni Fu, Zhaobo He, Yue Luo | Single-crystal sapphire (α-Al2O3) has been widely used in semiconductor, optics, communication, national defense, and other fields. Before application, an ultra-smooth surface which is scratch free and subsurface damage free is essential. Furthermore, the sapphire has unique qualities such as significant rigidity and chemical stability, which make it extremely arduous to process. Chemical mechanical polishing (CMP) is recognized as the final process to reduce the roughness and eliminate surface defects of a sapphire surface. In this review, the materials and equipment used for the chemical polishing of a sapphire wafer are summarized, and the surface nanoscale changes of sapphire wafer are reviewed from the angles of regulating polishing-process parameters, composition of polishing slurry including that which is nano-abrasive, a pH regulator, a complexing agent, and other additives, as well as hybrid CMP technologies. The outlook and future applications are also summarized. | 10.3390/coatings13122081 |
| Densely Packed Chemical Synthesis Equipment by 3D Spatial Design and Additive Manufacturing: Acetylene Generation Cartridge | Kirill S. Erokhin, Valentine P. Ananikov* | Preparation of and carrying out chemical reactions often require considerable laboratory space. Miniaturization of chemical equipment and reducing laboratory space requirements are an essential task to improve cost-efficiency, as well as increase safety and decrease potential risks. In this work, we discuss the miniaturization of laboratory equipment through the spatial optimization of functional parts. To demonstrate this, we have optimized the size of one of the most cumbersome processes in the lab, the generation of gases from solid sources. We have developed a ready-to-use acetylene generation cartridge with a high degree of functional filling of the internal space, which significantly compacts its dimensions. The cartridge was sealed upon three-dimensional (3D) printing, prepacked with the reagent, and found suitable for long-term storage. In addition to acetylene generation, built-in water trap channels and a drying compartment made it possible to obtain dried gas on the output of the cartridge. The cartridge showed high efficiency in the reactions for the synthesis of bis(arylthio)-substituted ethenes and butadienes. To expand the range of products based on acetylene, a procedure for the synthesis of 1,2-bis(alkylthio)ethenes was developed. The use of the cartridge allowed us to obtain target Z-ethenes with good yields and stereoselectivity. | 10.1021/acs.oprd.3c00112 |
| Exploring advanced process equipment visualization as a step towards digital twins development in the chemical industry: A CFD-DNN approach | DQ Gbadago, J Moon, S Hwang | Several studies involving the implementation of artificial neural network (ANN) technology for process design, monitoring, and control are under active research. This new technology has shown great potential in advancing chemical processes through the development of digital twins and smart factories. In joining this race, the current study explores the capability of physics-based modeling (CFD) and artificial neural networks for advanced process data visualization. Here, 20 CFD simulations of a multi-tubular reactor equipped with a Zn-Fe-Cr catalyst for synthesizing butadiene were executed. The simulation result was extracted as 3-D data with XYZ coordinates and imported into a python-based DNN model for training and cross-validation. An accuracy of 99.2% was obtained from the ANN surrogate model. The trained model was used to predict 3D data in terms of the process temperature, concentration, etc. The 3D data was then imported into a Paraview® VTK for detailed virtualization. Cross-sectional, longitudinal, and radial distribution of the various process variables, such as concentration profiles and pressure contour, were effectively visualized. A graphic user interface was further developed using Python for real-time visualization of the equipment. This implementation is analogous to the digital twin and is employable for online system optimization, high accuracy, low computational cost, and seamlessly integrable 3D real-time visualization system design for efficient, quick, and easy plant decision-making. | 10.1007/s11814-022-1273-2 |
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