Vortex motion‐based particle swarm optimisation for energy consumption of alumina evaporation

Zhu, Huangqiu; Chai, Qinqin; Yang, Chunhua; Wang, Xiaoli

doi:10.1002/cjce.21681

Cited by 3 publications

(14 citation statements)

References 48 publications

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“…The process consists of four falling film tube evaporators, three direct pre‐heaters, and three flash evaporators . The feed mother liquor discharged from the filtration process is pumped to the IIIrd and IVth evaporators through a feeding pump, and the output liquid coming from the IVth evaporator is pumped into the IIIrd pre‐heater and then goes into the IIIrd evaporator after being heated close to its boiling point.…”

Section: Optimization Model Of the Aepmentioning

confidence: 99%

“…Optimizing both of these can bring higher economic benefits in the actual evaporation process. Thus, in this section, we build a multi‐objective optimization model of AEP by introducing the exergy evaluation index into the optimization model of Zhu . The definitions of the indices, decision variables, and parameters are listed in the Nomenclature section.…”

Section: Optimization Model Of the Aepmentioning

confidence: 99%

“…Based on the material and mass balance principles, the mass of solute input into the i th unit equals the mass of solute going out of the i th unit. Thus, the formula of the material balance is described as follows:

F_{i} C_{i}^{j} = (F_{0} + F_{01}) C_{0}^{j}, i = 1,…,6, j = 1,2,3

F_{7} C_{7}^{j} = F_{0} C_{0}^{j}, j = 1,2,3

F_{i} = ϕ_{i} (Q_{i}, ρ_{i}, T_{i}, T_{r}, c p_{i}, H_{i}), i = 1,…,7

where

ϕ_{i} (\cdot)

represents the i th function relationship between F i and the operating variables (for a detailed description, see Equations in the optimization model of Zhu). Based on the data collected from a whole‐acid cycle of AEP under different scarring conditions at the China Aluminum Corporation,

ρ_{i}, c p_{i}, h_{i}, H_{i},

and T ni were determined by a regression method.…”

Section: Optimization Model Of the Aepmentioning

confidence: 99%

“…The AEP is an energy‐intensive process with complex energy efficiency relations and many interacting factors . There exists strong coupling between the mother liquor and the vapour steam from the multiple‐effect evaporators.…”

Section: Introductionmentioning

confidence: 99%

“…Khanam studied the effect of operating parameters on the steam consumption per tonne of evaporated water (SCPTEW), and a mixed integer nonlinear programming model was presented to schedule production and cleaning operations in a sugar plant with performance decay . Chai established a mathematical model of an evaporation system using a state‐space formulation with multiple time delays, and optimized the SCPTEW using particle swarm optimization (PSO), where the objective is to find a control law such that the specific quality of the mother liquor is met with the least energy usage, and the constraints imposed on the state and the control are satisfied. Although the optimization of AEP has enabled energy‐saving or cost‐saving techniques, it does not take into account operating costs and energy efficiency simultaneously, which can lead to unreasonable usage of high‐quality energy or inadequate production capacity.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm

Wang

Zhou

et al. 2015

Can J Chem Eng

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The alumina evaporation process (AEP) is an indispensable step for the reuse of sodium aluminate solution by evaporating excess water contained in the solution. The selection of optimal operating parameters is a complicated task because the process is influenced by many nonlinear factors when both the quality and quantity of the product are concerned. In this paper, we formulate a multi‐objective optimization model to maintain the balance of operating costs and energy efficiency in AEP, and a multi‐objective state transition algorithm (MOSTA) is proposed for solving this problem. With the aim of solving the constrained multi‐objective problem, a search archive strategy of elite populations and a novel infeasible solution replacement mechanism are integrated into STA. Some infeasible solutions with better performances are allowed to be saved and participate randomly in the evolution to select optimal solutions from all possible directions. A mutation operator is introduced into the evolutionary process to enhance the global search ability. Simulation results from some benchmark test problems show that the proposed method tends to converge quickly and effectively to the true Pareto frontier with better distribution. The proposed algorithm is successfully applied to solve the multi‐objective optimization problem arising in AEP. The optimal results show that operating costs and energy loss are considerably reduced, by approximately 13.63 % and 13.39 %, respectively.

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Section: Optimization Model Of the Aepmentioning

confidence: 99%

Section: Optimization Model Of the Aepmentioning

confidence: 99%

F_{i} C_{i}^{j} = (F_{0} + F_{01}) C_{0}^{j}, i = 1,…,6, j = 1,2,3

F_{7} C_{7}^{j} = F_{0} C_{0}^{j}, j = 1,2,3

F_{i} = ϕ_{i} (Q_{i}, ρ_{i}, T_{i}, T_{r}, c p_{i}, H_{i}), i = 1,…,7

where

ϕ_{i} (\cdot)

ρ_{i}, c p_{i}, h_{i}, H_{i},

and T ni were determined by a regression method.…”

Section: Optimization Model Of the Aepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm

Wang

Zhou

et al. 2015

Can J Chem Eng

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Data reconciliation strategy with time registration for the evaporation process in alumina production

et al. 2017

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In real industrial production, many mass and heat transfer processes are influenced by high temperature, high pressure, and even strong acid or alkali conditions. In addition, some important variables cannot be measured and chemical compositions are analyzed offline with a long time delay, which leads to inaccurate measurements of the process data. In this paper, a layered data reconciliation (LDR) method based on time registration is proposed to improve the measurement accuracy and estimate unmeasured variables. Considering that the material cannot be tagged and tracked in process manufacturing, a temporal and spatial matching strategy for the process data is designed based on a time-correlation analysis matrix which is determined to describe the correlation of each time sequence in the data matrix. Then, a layered data reconciliation model with time registration is developed by reconciling the mass balance layer and the heat balance layer separately and stepwise, and the model is solved by the state transition algorithm. Meanwhile, regular terms and engineer's knowledge are introduced into the data reconciliation model to solve the problem of insufficient redundancy. The industrial verification results from the actual industrial evaporation process indicate that the accuracy of measured values is improved by using the proposed reconciliation strategy.

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A neighborhood‐adaptive state transition algorithm for operational optimization of residue hydrogenation fractionation process

Wang

et al. 2021

Int J Energy Res

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Residual hydrogenation fractionation process (RHFP) is significant for extracting valuable fractions from heavy oil with a severe energy consumption. The operational optimization of RHPF for comprehensively improving product profits and reducing energy consumption costs has been studied in this paper. Considering the complex nonlinear dynamic constraints, a novel neighborhood-adaptive state transition algorithm (NaSTA) is proposed. Specifically, a variable localneighborhood strategy is designed to adaptively change the size of the neighborhood and speed up the initial search. Then, randomly generating large-scope candidate solutions aids the algorithm to jump out of the local optimal solution.In addition, an out-of-bound candidate solution processing mechanism is proposed to reasonably allocate candidate solutions that exceed the predefined neighborhood boundaries. The statistical research manifests the superiority of the proposed method compared with other optimization algorithms. The experimental results verify that the energy consumption is considerably reduced by approximately 10.06% so that the overall profit is improved.

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Vortex motion‐based particle swarm optimisation for energy consumption of alumina evaporation

Cited by 3 publications

References 48 publications

Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm

Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm

Data reconciliation strategy with time registration for the evaporation process in alumina production

A neighborhood‐adaptive state transition algorithm for operational optimization of residue hydrogenation fractionation process

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