Multiple
cracking furnaces in an ethylene plant run in parallel
to produce ethylene, propylene, and other products from different
hydrocarbon feedstocks. Because both coke formation in radiant coils
and change of operation conditions with time have significant effects
on the performance of cracking furnaces, it is better for the cyclic
scheduling to be simultaneously optimized with the operation conditions.
To match this real requirement, a mixed-integer dynamic optimization
(MIDO) problem is presented for the optimization of both operation
conditions and cyclic scheduling simultaneously, through which the
optimal assignment, the processing time, the subcycle number, and
the optimal operation conditions of different feeds in different cracking
furnaces are determined at the same time. To solve the MIDO problem,
it is discretized and converted into a large scale mixed-integer nonlinear
programming (MINLP) problem. The two scheduling cases of a cracking
furnaces system are illustrated showing a remarkable increase in the
economic performance as compared with that of the traditional method.
In surrogate modelling, a simple functional approximation of a complex system model is always constructed to reduce the computational expense, and the selection of a suitable surrogate model and a sampling method are key to obtaining a surrogate model for a complex system. To construct an appropriate surrogate model, three methods of adaptive surrogate modelling that use artificial neural networks (ANN) are developed by incorporating a new mechanism for automatically determining the number of hidden nodes and/or a new prediction error-based mixed adaptive sampling method. In the automatic determination, the number of hidden nodes can adaptively change according to the effective rate of parameters in the ANN during the adaptive surrogate modelling process. As a result, an improper number of hidden nodes determined by the empirical method can be avoided. The prediction error-based mixed adaptive sampling method is capable of finding the strong nonlinear behaviour of the underlying system, which is easily missed by the traditional prediction variance-based sampling method. The three methods and the previous method for adaptive surrogate modelling that use ANN are tested and compared in terms of replicating the behaviours of three types of challenge functions to determine the efficacy of the developed methods. Furthermore, these methods are used in an engineering problem of surrogate modelling for a cracking reaction simulator to validate the efficacy of the developed methods.
A simple pseudo-dynamic surrogate model is developed in the framework of the state space model with the feed-forward neural network to replace the complex free radical pyrolysis model. The surrogate model is then applied to investigate the multi-objective optimization of two key performance objectives with distinct contradiction: the mean yields of key products and the day mean profits. The e-constraint method is employed to solve the multi-objective optimization problem, which provides a broad range of operation conditions depicting tradeoffs of both key objectives. The Pareto-optimal frontier is successfully obtained and five selected cases on the frontier are discussed, suggesting that flexible operations can be performed based on industrial demands.
The abandoned concrete block after crushing, washing, screening, and then blended into line in accordance with a certain percentage of recycled coarse aggregate particle size distribution in proportion to the distribution of alternative native coarse aggregate, recycled concrete is formulated for recycling waste concrete and effective way. Under study and adopt the same water-cement ratio of state, adding different proportions of recycled coarse aggregate to analyze the intensity variation between blended with recycled concrete and ordinary concrete proportions.
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