Multi-objective modeling and optimization for scheduling of cracking furnace systems

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.

Strategic mining options optimization:Open pit mining, underground mining or both

Near-surface deposits that extend to considerable depths are often amenable to both open pit mining and/or underground mining. This paper investigates the strategy of mining options for an orebody using a Mixed Integer Linear Programming(MILP) optimization framework. The MILP formulation maximizes the Net Present Value(NPV) of the reserve when extracted with(i) open pit mining,(ii) underground mining, and(iii) concurrent open pit and underground mining. Comparatively, implementing open pit mining generates a higher NPV than underground mining. However considering the investment required for these mining options, underground mining generates a better return on investment than open pit mining. Also, in the concurrent open pit and underground mining scenario, the optimizer prefers extracting blocks using open pit mining. Although the underground mine could access ore sooner, the mining cost differential for open pit mining is more than compensated for by the discounting benefits associated with earlier underground mining.

Modeling and optimization for oil well production scheduling

In this paper,an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied.The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wells in a given oil reservoir,subject to a number of constraints such as minimum up/down time limits and well grouping.The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost.Due to the NP-hardness of the problem,an improved particle swarm optimization(PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately.Computational experiments on randomly generated instances were carried out to evaluate the performance of the model and the algorithm's effectiveness.Compared with the commercial solver CPLEX,the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.

End-to-end energy-efficient resource allocation in device-to-device communication underlaying cellular networks

A proposed resource allocation （RA） scheme is given to device-to-device （D2D） communication underlaying cellular networks from an end-to-end energy-efficient perspective, in which, the end-to-end energy consumptions were taken into account. Furthermore, to match the practical situations and maximize the energy-efficiency （EE）, the resource units （RUs） were used in a complete-shared pattern. Then the energy-efficient RA problem was formulated as a mixed integer and non-convex optimization problem, extremely difficult to be solved. To obtain a desirable solution with a reasonable computation cost, this problem was dealt with two steps. Step 1, the RU allocation policy was obtained via a greedy search method. Step 2, after obtaining the RU allocation, the power allocation strategy was developed through quantum-behaved particle swarm optimization （QPSO）. Finally, simulation was presented to validate the effectiveness of the proposed RA scheme.