Selective esterification of glycerol with acetic acid to diacetin using antimony pentoxide as reusable catalyst

Glycerol can be obtained as a by-product during biodiesel manufacture. It is important to convert glycerol to value-added products. Glycerol esterification with acetic acid is one of the most promising approaches for glycerol utilization. It is usually difficult to obtain diacetin with good activity and selectivity. In this work, glycerol esterification with acetic acid over different metal oxides, such as Bi2O3, Sb2O3, SnO2, TiO2, Nb2O5 and Sb2O5, was investigated. It was found that in the six investigated metal oxides, only Sb205 resulted in good activity and selectivity to diacetin. Under the optimized conditions, the glycerol conversion reached 96.8%, and the selectivity to diacetin reached 54.2%, while the selectivity to monoacetin and triacetin was 33.2% and 12.6%, respectively. The catalysts were characterized with FT-IR spectra of adsorbed pyridine, which indicated that in the six investigated metal oxides, only Sb2O5 possessed Bronsted acid sites strong enough to protonate adsorbed pyridine. The good catalytic activity and selectivity to diacetin might be mainly attributable to the Bronsted acid sites of Sb2O5. Reusability tests showed that with 5b205 as catalyst, after six reaction cycles, no significant change in the glycerol conversion and the selectivity to diacetin was observed.

An Early-Stop Adjoint Transient Sensitivity Analysis Method for Objective Functions Associated with Many Time Points

Transient sensitivity analysis aims to obtain the gradients of objective functions(circuit performance)with respect to design or variation parameters in a simulator,which can be widely used in yield analysis and circuit optimization,among others.However,the traditional method has a computational complexity of O(N^(2))for objective functions containing circuit states at N time points.The computational complexity is too expensive for large N,especially in time-frequency transform.This paper proposes a many-time-point sensitivity method to reduce the computational complexity to O(N)in multiparameter many-time-point cases.The paper demonstrates a derivation process that improves efficiency by weighting the transfer chain and multiplexing the backpropagation process.We also proposed an early-stop method to improve efficiency further under the premise of ensuring accuracy.The algorithm enables sensitivity calculation of performances involving thousands of time points,such as signal-to-noise and distortion ratio and total harmonic distortion,with significant speed improvements.

Building a Post-Layout Simulation Performance Model with Global Mapping Model Fusion Technique

Building a post-layout simulation performance model is essential in closing the loop of analog circuits, but it is a challenging task because of the high-dimensional space and expensive simulation cost. To facilitate efficient modeling, this paper proposes a Global Mapping Model Fusion(GMMF) technique. The key idea of GMMF is to reuse the schematic-level model trained by the Artificial Neural Network(ANN) algorithm, and combine it with few mapping coefficients to build the post-simulation model. Furthermore, as an efficient global optimization algorithm,differential evolution is applied to determine the optimal mapping coefficients with few samples. In GMMF, only a small number of mapping coefficients are unknown, so the number of post-layout samples needed is significantly reduced. To enhance practical utility of the proposed GMMF technique, two specific mapping relations, i.e., linear or weakly no-linear and nonlinear, are carefully considered in this paper. We conduct experiments on two topologies of two-stage operational amplifier and comparator in different commercial processes. All the simulation data for modeling are obtained from a parametric design framework. A more than 5 runtime speedup is achieved over ANN without surrendering any accuracy.