Nonsharp distillation sequences are widely used in industrial separation processes; however, most current research has not discussed this topic, except in sequences with heat integration under special operating conditions, including complex columns. The sequence with nonsharp separation has the features of general distillation sequences, which are usually optimized by adjusting the separation sequence and the design/operation parameters of each column in the sequence, making the optimization a mixed integer nonlinear programming (MINLP) problem, which is usually hard to solve. With inclusion of nonsharp separation columns, the sequence optimization becomes even more complicated and computationally intensive. This work aimed to optimize the distillation sequence, including nonsharp distillation alongside simple columns and dividing wall columns. Inspired by the dynamic programing method for sharp distillation sequence, a framework for automatic optimization is proposed to decompose the MINLP problem into integer programming (IP) and nonlinear programming (NLP) problems. The optimization processes of sharp and nonsharp distillation sequences are compared and the solution space in terms of the possible number of distillation sequences with nonsharp separation is discussed. Two optimization cases, including an industrial one, are included to validate the proposed framework.
Flow separation control has a wide application prospect in drag reduction for industry. This paper numerically studies the effect of microstructures on flow separation and drag reduction. Simple morphological microstructures, derived from the tilted shark scales, are attached to the wing at an angle of attack. The spacing and height of microstructures are made dimensionless by using the microstructure width and half of the wing width, respectively, that is, d̃m=dm/dAB and h̃m=hm/(H/2). The angle of attack is set to 10°. It is found that microstructures can reduce the motion amplitude of shed vortices, thereby suppressing flow separation and reducing drag. Both the planar and curved microstructures have excellent drag reduction performance. The microstructure spacing d̃m and tilt angle θ should not be too large or too small; otherwise, it will weaken the drag reduction ability. Cases d̃m=1.51, θ=20°, and θ=30° exhibit excellent drag reduction performance. The microstructure has the characteristic for being small, yet it needs to reach a certain height h̃m to effectively reduce drag. The case h̃m=0.667 is the most superior choice. Based on the proposed microstructure shape and spacing, the drag reduction performance of microstructures can reach more than 28%. Meanwhile, the drag reduction performance of microstructures increases with the improvement of the attachment proportion pm, and case pm≥50% is suggested for significant drag reduction performance. Finally, we discuss the drag reduction performance of microstructures on the wing at different angles of attack and find that microstructures can achieve good drag reduction, provided that the pressure drag caused by the flow separation is a significant proportion of the total drag and the flow separation occurs within the controllable range of microstructures.
Parallel computing has been developed for many years in chemical process simulation. However, existing research on parallel computing in dynamic simulation cannot take full advantage of computer performance. More and more applications of data-driven methods and increasing complexity in chemical processes need faster dynamic simulators. In this research, we discuss the upper limit of speed-up for dynamic simulation of the chemical process. Then we design a parallel program considering the process model solving sequence and rewrite the General dynamic simulation & optimization system (DSO) with two levels of parallelism, multithreading parallelism and vectorized parallelism. The dependency between subtasks and the characteristic of the hottest subroutines are analyzed. Finally, the accelerating effect of the parallel simulator is tested based on a 500 kt·a−1 ethylbenzene process simulation. A 5-hour process simulation shows that the highest speed-up ratio to the original program is 261%, and the simulation finished in 70.98 s wall clock time.
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