scholarly journals Application of Sequential Quadratic Programming Based on Active Set Method in Cleaner Production

2021 ◽  
Author(s):  
Li Xia ◽  
JianYang Ling ◽  
Zhen Xu ◽  
Rongshan Bi ◽  
Wenying Zhao ◽  
...  
Keyword(s):  
Quadratic Programming ◽  
Process Simulation ◽  
Sequential Quadratic Programming ◽  
Chemical Process ◽  
Simulation Software ◽  
Active Set ◽  
Active Set Method ◽  
General Chemical ◽  
Optimization Function ◽  
Speed Up

Abstract On the platform of general chemical process simulation software(it was named Optimization Engineer, OPEN), a general optimization algorithm for chemical process simulation is developed using C++ code. The algorithm is based on Sequential Quadratic Programming (SQP). We adopt the activity set algorithm and the rotation axis algorithm to generate the activity set to solve the quadratic programming sub-problem. The active set method can simplify the number of constraints and speed up the calculation. At the same time, we used limited memory BFGS algorithm (L-BFGS) to simplify the solution of second derivative matrix. The special matrix storage mode of L-BFGS algorithm can save the storage space and speed up the computing efficiency. We use exact penalty function and traditional step-size rule in the algorithm. These two methods can ensure the convergence of the algorithm, a more correct search direction and suitable search step. The example shows that the advanced optimization function can meet the requirements of General Chemical Process Calculation. The number of iterations can reduce by about 6.0% . The computation time can reduce by about 6.5% . We combined this algorithm with chemical simulation technology to develop the optimization function of chemical engineering simulation. This optimization function can play an important role in the process optimization calculation aiming at energy saving and green production.

scholarly journals A Parallel Processing Approach to Dynamic Simulation of Ethylbenzene Process

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1386
Author(s):  
Junkai Zhang ◽  
Zhongqi Liu ◽  
Zengzhi Du ◽  
Jianhong Wang
Keyword(s):  
Parallel Computing ◽  
Dynamic Simulation ◽  
Process Simulation ◽  
Chemical Process ◽  
Process Model ◽  
Chemical Processes ◽  
Data Driven ◽  
Computer Performance ◽  
Speed Up ◽  
General Dynamic

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.

An Active-Set Method for Quadratic Programming Based On Sequential Hot-Starts

2015 ◽  
Vol 25 (2) ◽  
pp. 967-994 ◽  
Author(s):  
Travis C. Johnson ◽  
Christian Kirches ◽  
Andreas Wächter
Keyword(s):  
Quadratic Programming ◽  
Active Set ◽  
Active Set Method

scholarly journals Hybrid PIPSO-SQP Algorithm for Real Power Loss Minimization in Radial Distribution Systems with Optimal Placement of Distributed Generation

2020 ◽  
Vol 12 (14) ◽  
pp. 5787
Author(s):  
S. Angalaeswari ◽  
P. Sanjeevikumar ◽  
K. Jamuna ◽  
Zbigniew Leonowicz
Keyword(s):  
Quadratic Programming ◽  
Radial Distribution ◽  
Power Loss ◽  
Distribution Systems ◽  
Optimal Placement ◽  
Loss Minimization ◽  
Active Set ◽  
Active Set Method ◽  
Real Power ◽  
Power Loss Minimization

This paper proposes the hybrid sequential quadratic programming (SQP) technique based on active set method for identifying the optimal placement and rating of distribution generation (DG) incorporated in radial distribution systems (RDS) for minimizing the real power loss satisfying power balance equations and voltage limits. SQP runs quadratic programming sequentially as a sub-program to obtain the best solution by using an active set method. In this paper, the best optimal solution is selected with less computation time by combining the benefits of both classical and meta-heuristic methods. SQP is a classical method that is more sensitive to initial value selection and the evolutionary methods give approximate solution. Hence, the initial values for the SQP technique were obtained from the meta–heuristic method of Parameter Improved Particle Swarm Optimization (PIPSO) algorithm. The proposed hybrid PIPSO–SQP method was implemented in IEEE 33-bus RDS, IEEE 69-bus RDS, and IEEE 118-bus RDS under different loading conditions. The results show that the proposed method has efficient reduction in real power loss minimization through the enhancement of the bus voltage profile.

scholarly journals An active set sequential quadratic programming algorithm for nonlinear optimisation

2006 ◽  
Vol 74 (1) ◽  
pp. 69-83
Author(s):  
Qing-Jie Hu ◽  
Yun-Hai Xiao ◽  
Y. Chen
Keyword(s):  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Optimization Problems ◽  
Inequality Constraints ◽  
Least Square ◽  
Programming Algorithm ◽  
Active Set ◽  
Nonlinear Inequality ◽  
Nonlinear Inequality Constraints ◽  
Sequential Quadratic Programming Algorithm

In this paper, we have proposed an active set feasible sequential quadratic programming algorithm for nonlinear inequality constraints optimization problems. At each iteration of the proposed algorithm, a feasible direction of descent is obtained by solving a reduced quadratic programming subproblem. To overcome the Maratos effect, a higher-order correction direction is obtained by solving a reduced least square problem. The algorithm is proved to be globally convergent and superlinearly convergent under some mild conditions without strict complementarity.

Active-set sequential quadratic programming with variable probabilistic constraint evaluations for optimization problems under non-Gaussian uncertainties

2010 ◽  
Vol 224 (6) ◽  
pp. 1273-1285 ◽  
Author(s):  
K-Y Chan ◽  
Y-C Huang
Keyword(s):  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Optimization Problems ◽  
Probabilistic Constraints ◽  
Minimal Distance ◽  
Active Set ◽  
Structural Problems ◽  
Design Iteration ◽  
Design Variables ◽  
Non Gaussian

Design optimization under random uncertainties are formulated as problems with probabilistic constraints. Calculating these constraints presents a major challenge in the optimization. While most research concentrates on uncertainties that are Gaussian, a great number of uncertainties in the environment are non-Gaussian. In this work, various reliability analyses for non-Gaussian uncertainties within a sequential quadratic programming framework are integrated. An analytical reliability contour (RC) is first constructed in the design space to indicate the minimal distance from the feasible boundary of a design at a desired reliability level. A safe zone contour is then created so as to provide a quick estimate of the RC. At each design iteration reliability analyses of different accuracies are selected based on the level needed, depending on the activity of a constraint. For problems with a large number of constraints and relatively few design variables, such as structural problems, the active set strategies significantly improve efficiency, as demonstrated in the examples.

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