The real alkaline cleaning wastewater (ACW) was treated by a process consisting of neutralization, NaClO oxidation and aluminum sulfate (AS) coagulation, and a novel response surface methodology coupled nonlinear prog...
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The real alkaline cleaning wastewater (ACW) was treated by a process consisting of neutralization, NaClO oxidation and aluminum sulfate (AS) coagulation, and a novel response surface methodology coupled nonlinear programming (RSM-NLP) approach was developed and used to optimize the oxidation coagulation process under constraints of relevant discharge standards. Sulfuric acid neutralization effectively removed chemical oxygen demand (COD), surfactant alkylphenol ethoxylates (OP-10) and silicate at the optimum pH of 7.0, with efficiencies of 62.3%, >82.7% and 94.2%, respectively. Coagulation and adsorption by colloidal hydrated silica formed during neutralization were the major removal mechanisms. NaClO oxidation achieved almost complete removal of COD, but was ineffective for the removal of surfactant OP-10. AS coagulation followed by oxidation can efficiently remove OP-10 with the formation of Si-O-Al compounds. The optimum conditions for COD <= 100 mg/L were obtained at hypo chlorite to COD molar ratio of 2.25, pH of 10.0 and AS dosage of 0.65 g Al/L, with minimum cost of 9.58 $/m(3) ACW. This study shows that the integrative RSM-NLP approach could effectively optimize the oxidation-coagulation process, and is attractive for techno-economic optimization of systems with multiple factors and threshold requirements for response variables. (C) 2017 Elsevier Ltd. All rights reserved.
作者：
Kim, MinjaeMyongji Univ
Dept Mech Engn 116 Myongji Ro Yongin 17058 Gyeonggi Do South Korea
Conventional developed component matching methods for a series type hybrid electric vehicle have a high computational burden or component alternation researches have considered only a few parts without the weight vari...
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Conventional developed component matching methods for a series type hybrid electric vehicle have a high computational burden or component alternation researches have considered only a few parts without the weight variation of each component. To address such problems, this study presents a novel component matching method with nonlinear programming (NLP) for a series hybrid electric bus. The fuel consumption minimization problem is discretized in time and multistarting points are used with the variations of each component. The proposed matching method suggests to use novel initial standards for component matching such that both the computational efficiency and accuracy could be achieved simultaneously. As a result, the most fuel efficient component combination among 8 components could be found, where the results were verified with those of dynamic programming (DP).
Coal chemical industry plays a critical role in China’s economic growth and energy security. However, its carbon-intensity characteristics cause a large number of CO 2 emissions during coal chemicals production. Faci...
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Coal chemical industry plays a critical role in China’s economic growth and energy security. However, its carbon-intensity characteristics cause a large number of CO 2 emissions during coal chemicals production. Facing intense pressure to reduce CO 2 emissions, it is urgent to seek synergistic development between CO 2 emissions reduction and coal chemical engineering. A nonlinear programming (NLP) approach is proposed to optimize the deployment of China’s coal chemical industry under carbon constraints. The NLP model is pursuing the minimum CO 2 emission per unit value of gross output of coal to chemicals sector (CPUVGC) with simultaneously satisfying economic growth. Twelve main categories coal chemical products and six measures or technologies of CO 2 emission reduction are taken into consideration in the NLP model, based on which a short-term (2020), mid-term (2030) and long-term (2050) deployment of coal chemical industry under restriction of CO 2 emissions are investigated, and sensitivity or uncertainty analysis of effects of crude oil price (COP), which have a significant impact on coal chemicals price, on CO 2 emission reduction target also is performed. Three scenarios involved 100% (positive), 50% (moderate) and 25% (conservative) of the predicted target of CO 2 emissions reduction from different technologies or measures of CO 2 emissions reduction are analyzed in different periods. At the end, the development roadmap (2020-2030-2050) of coal chemical industry under carbon constraints is plotted and some specific suggestions and safeguard measures are also provided to guarantee implement of the planning.
In this paper, a mixed integer nonlinear programming model is proposed to concurrently design two segments (i.e., upstream and midstream) of crudel oil supply chain. The network includes all entities and their connect...
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In this paper, a mixed integer nonlinear programming model is proposed to concurrently design two segments (i.e., upstream and midstream) of crudel oil supply chain. The network includes all entities and their connections from oil wells to product depots. Furthermore, a real world example is applied to show the improved model application. Furthermore, a sensitivity analysis in which +/- 20% deviations at a time were placed on two parameters is presented. Also, model performance is analyzed with GAMS 22.6. The proposed multiperiod and multiproduct model consists of several decisions (i.e., oil field development, transformation, transportation, and distribution). The main contributions of this work are inclusion of all entities related to upstream and midstream segments and both oil field development and transformation planning, simultaneously. Finally, it is shown that a decrease in production cost of refinery products will lead to more net profit given all refinery production capacity are used. Also, increase in refinery production capacity will improve network net profit given new fixed cost investment is not applied (e.g., refineries and transportation modes). This is the first study that simultaneously considers and optimizes upstream and midstream of crude oil supply chain. Second, it presents a unique mathematical model. Third, all features and parameters are included. Fourth, it is practical and may be used for other crude oil supply chain.
We construct a family of globally defined dynamical systems for a nonlinear programming problem, such that (a) the equilibrium points are the unknown (and sought) critical points of the problem, (b) for every initial ...
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We construct a family of globally defined dynamical systems for a nonlinear programming problem, such that (a) the equilibrium points are the unknown (and sought) critical points of the problem, (b) for every initial condition, the solution of the corresponding initial value problem converges to the set of critical points, (c) every strict local minimum is locally asymptotically stable, (d) the feasible set is a positively invariant set, and (e) the dynamical system is given explicitly and does not involve the unknown critical points of the problem. No convexity assumption is employed. The construction of the family of dynamical systems is based on an extension of the control Lyapunov function methodology, which employs extensions of LaSalle's theorem and are of independent interest. Examples illustrate the obtained results.
In the context of sequential methods for solving general nonlinear programming problems, it is usual to work with augmented subproblems instead of the original ones. This paper addresses the theoretical reasoning behi...
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In the context of sequential methods for solving general nonlinear programming problems, it is usual to work with augmented subproblems instead of the original ones. This paper addresses the theoretical reasoning behind handling the original subproblems by an augmentation strategy related to the differentiable reformulation of the-penalized problem. Nevertheless, this paper is not concerned with the sequential method itself, but with the features about the original problem that can be inferred from the properties of the solution of the augmented problem. Moreover, no assumption is made upon the feasibility of the original problem, neither about the fulfillment of any constraint qualification, nor of any regularity condition, such as calmness. The convergence analysis of the involved sequences is presented, independent of the strategy employed to produce the iterates. Examples that elucidate the interrelations among the obtained results are also provided.
The focus of this work is on analyzing and developing nonlinear solvers for performing nonlinear structural analysis for large displacements in both elastic and inelastic cases. The response of a structure to a load a...
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The focus of this work is on analyzing and developing nonlinear solvers for performing nonlinear structural analysis for large displacements in both elastic and inelastic cases. The response of a structure to a load application is shown by its equilibrium path which may include snap back and snap through behavior. Material and geometric nonlinearities are taken into consideration while developing the response path of a multi-element structure with sections discretized using fiber elements. Traditionally, Newton’s method is employed for solving the system of nonlinear equations but it comes with certain challenges. The response determination becomes difficult when stiffness matrix becomes singular at turning point. It also requires the calculation of the inverse of a Hessian matrix, which is costly. Newton’s method gives quadratic convergence but as the scale of the structure increases, resorting to Newton’s method becomes *** limitations motivate us to explore new solvers. Hence, in this study we analyze and develop various nonlinearly constrained optimization solvers for a recently suggested hybrid finite element. In particular, we compare the performance of conjugate gradient method with or without preconditioning, Sequential Quadratic programming method and augmented Lagrangian method. For the case of structural response with snap back and snap through behavior, a new method called the implicit path continuation method is developed to ensure path continuation and solution convergence. The various solvers are then validated by obtaining responses of three benchmark structural problems with large displacements and rotations, and comparing the results with the conventional Newton’s method and a variant of Newton’s method with submatrices.
The problem of packing ellipsoids is considered in the present work. Usually, the computational effort associated with numerical optimization methods devoted to packing ellipsoids grows quadratically with respect to t...
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The problem of packing ellipsoids is considered in the present work. Usually, the computational effort associated with numerical optimization methods devoted to packing ellipsoids grows quadratically with respect to the number of ellipsoids being packed. The reason is that the number of variables and constraints of ellipsoids' packing models is associated with the requirement that every pair of ellipsoids must not overlap. As a consequence, it is hard to solve the problem when the number of ellipsoids is large. In this paper, we present a nonlinear programming model for packing ellipsoids that contains a linear number of variables and constraints. The proposed model finds its basis in a transformation-based non-overlapping model recently introduced by Birgin et al. (J Glob Optim 65(4):709-743, 2016). For solving large-sized instances of ellipsoids' packing problems with up to 1000 ellipsoids, a multi-start strategy that combines clever initial random guesses with a state-of-the-art (local) nonlinear optimization solver is presented. Numerical experiments show the efficiency and effectiveness of the proposed model and methodology.
An inexact restoration derivative-free filter method for nonlinear programming is introduced in this paper. Each iteration is composed of a restoration phase, which reduces a measure of infeasibility, and an optimizat...
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An inexact restoration derivative-free filter method for nonlinear programming is introduced in this paper. Each iteration is composed of a restoration phase, which reduces a measure of infeasibility, and an optimization phase, which reduces the objective function. The restoration phase is solved using a derivative-free method for solving underdetermined nonlinear systems with bound constraints, developed previously by the authors. An alternative for solving the optimization phase is considered. Theoretical convergence results and some preliminary numerical experiments are presented.
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