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This paper considers the numerical approximation for the optimal supporting position and related optimal control of a catalytic reaction system with some control and state constraints, which is governed by a nonlinear partial differential equations with given initial and boundary conditions. By the Galerkin finite element method, the original problem is projected into a semi-discrete optimal control problem governed by a system of ordinary differential equations. Then the control parameterization method is applied to approximate the control and reduce the original system to an optimal parameter selection problem, in which both the position and related control are taken as decision variables to be optimized. This problem can be solved as a nonlinear optimization problem by a particle swarm optimization algorithm. The numerical simulations are given to illustrate the effectiveness of the proposed numerical approximation method. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems are difficult to solve in that the optimized objectives are usually conflicting with each other. It is usually hard to find an optimal solution that satisfies all objectives from the mathematical point of view. Unlike analytical methods and classical numerical methods, which require strict mathematical calculation or defined initial search values, intelligent optimization algorithms are heuristic algorithms able to find global optimal solutions. In this paper, we make a brief introduction of multi-objective optimization problems and some state-of-the-art intelligent algorithms. In order to get the final optimal solution in the real-world multi-objective optimization problems, trade-off methods including a priori methods, interactive methods, Pareto-dominated methods and new dominance methods are utilized. Moreover, we give a review of multi-objective optimization methods application in the environmental protection fields, for optimization objectives of energy saving, emissions reduction and cost reduction, etc. At last, a whole summary about current difficulties existed in the multi-objective optimization problem is given out, serving as suggestions or guidance for future researches. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A novel AP-DEACM method is proposed. • Performance evaluation and energy optimization model is obtained. • This proposed model is efficient in energy saving and carbon emission reduction. • The energy saving potential of ethylene and PTA production plants are 2.78% and 1.26%. • Carbon emission reduction potential is 3.62% in ethylene production plants. Abstract Data Envelopment Analysis (DEA) has been widely used in performance and energy efficiency evaluation. However, in the traditional DEA, the effective of each decision making unit (DMU) is evaluated through its own optimized perspective and regardless of other DMUs influence, which may result in too many effective DMUs. And the DEA cross-model (DEACM) can distinguish the effective DMUs better by constructing a cross-efficiency matrix, but the optimal weight of the DMU may not be unique, so the cross efficiency of the DEACM may be different. Therefore, this paper proposes a novel DEACM based on the affinity propagation (AP) clustering algorithm (AP-DEACM). Through the AP clustering algorithm, the high impact data affecting the performance capacity and energy saving are obtained. Then the better effective DMU is identified through the high discrimination of the AP–DEACM. Finally, the proposed AP-DEACM is used for performance evaluation and energy optimization modeling of the Pure Terephthalic Acid (PTA) production process and the ethylene industrial process in complex petrochemical industries. The experimental results show that the energy saving potential of PTA production plants and ethylene production plants are 2.78% and 1.26%, respectively, and the average value of carbon emission savings potential is 3.62% in ethylene production plants. [ABSTRACT FROM AUTHOR]

Energy optimization and analysis of complex chemical processes play a significant role in the sustainable development procedure. In order to deal with the high-dimensional and noise data in complex chemical processes, we present an energy optimization and analysis method based on extreme learning machine integrating the index decomposition analysis. First, index decomposition analysis has been used to decompose the high-dimensional data to three energy performance indexes of the activity effect, the structure effect and the intensity. And then, those indexes and the production/conductivity of the chemical process are defined as inputs and outputs of the extreme learning machine respectively to build energy optimization and analysis model. Finally, the proposed method has been applied to optimizing and analyzing energy status of the ethylene system and the purified terephthalic acid solvent system in complex chemical processes. The experiment results show that the proposed method has the characteristics of fast learning, stable network outputs and high model accuracy in handling with the high-dimensional data. Moreover, it can optimize energy of chemical processes and guide the production operation. In our experiment, the production of ethylene plants can be increased by 5.33%, and the conductivity of purified terephthalic acid plants can be reduced by 0.046%. [ABSTRACT FROM AUTHOR]

Abstract: Two new types of hierarchy probability cost analysis (HPCA) model incorporating money allocated is money spent (MAIMS) principle based on definite work breakdown structure (WBS) level for EPC (engineering, procurement and construction) projects are presented. The proposed models have skillfully solved dilemma to appropriate cost elements and maximize the efficiency of information for cost risk analysis. Macroscopic and microscopic risk analysis of the project cost elements are introduced for meaningful model input. The illustration of an actual bidding EPC project substantiates that proposed integrating HPCA-hierarchy MAIMS models have demonstrated effective and viable for EPC projects. [Copyright &y& Elsevier]