Liquid-phase 1-butene hydroisomerization to 2-butene
is an important
approach to utilize cheap and abundant C4 olefins (normally as by-products
from refinery processes), and the reaction kinetics is critical to
the development of its reactors and catalysts. Herein, kinetics experiments
are performed to understand the characteristics of liquid-phase 1-butene
hydroisomerization over a commercial Pd/Al2O3 catalyst, and three intrinsic kinetics models are proposed to describe
the rates of the hydroisomerization and hydrogenation reactions. The
results show that the selectivity toward 2-butene increases with temperature,
indicating the hydrogenation reactions are suppressed due to the low
partial pressure of hydrogen under high temperature. The fraction
of hydrogen in reactants significantly affects 1-butene conversion
and 2-butene selectivity, and thus, this effect should be included
in kinetics models. Comparing the proposed power-law, dual-site Langmuir–Hinshelwood,
and single-site Langmuir–Hinshelwood models, the single-site
Langmuir–Hinshelwood model is the best in predicting the experiments,
implying that hydroisomerization and hydrogenation reactions may occur
at the same type of active sites under the reaction conditions in
this work. The activation energies of the hydroisomerization reactions
are lower than those of the hydrogenation reaction, and the adsorption
enthalpy of butenes is smaller than that of hydrogen.
This paper proposes a novel material recognition method for robotic tactile sensing. The method is composed of two steps. Firstly, a human-touch-inspired short-duration (1 s) slide action is conducted by the robot to obtain the tactile data. Then, the tactile data is processed with a machine learning algorithm, where 11 bioinspired features were designed to imitate the mechanical stimuli towards the four main types of tactile receptors in the skin. In this paper, a material database consisting of 144,000 tactile images is used to train seven classifiers, and the most accurate classifier is selected to recognize 12 household objects according to their properties and materials. In the property recognition, the materials are classified into 4 categories according to their compliance and texture, and the best accuracy reaches 96% in 36 ms. In the material recognition, the specific materials are recognized, and the best accuracy reaches 90% in 37 ms. The results verify the effectiveness of the proposed method.
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