炼油厂全流程优化建模方法研究

为了克服使用线性规划和单处理单元非线性规划进行生产优化的内在缺点，在建立原油单元、催化裂化、催化重整和汽油调合等4个重要处理单元详细模型的基础上，将单处理单元非线性过程模型集成到一个全流程优化非线性模型中。优化的目标函数是使炼油厂全厂利润最大。决策变量是那些显著影响全厂效益的变量，约束条件是单个处理单元约束条件的集合。对某炼油厂进行实例研究，结果表明该全流程模型能有效处理全厂优化问题，所得利润在线性规划的基础上提高4．5％。     

渤海绥中油田J3井疏水缔合聚合物驱数值模拟研究

为了进一步提高渤海油田J3井区缔合聚合物驱的技术经济效果,在渤海油田室内配方和先导性矿场试验研究基础上,利用化学驱油藏数值模拟软件FAPMS,分析和研究了高浓度缔合聚合物前置段塞、主段塞的大小和浓度以及段塞组合方式对驱油效果的影响,优化设计了渤海绥中油田J3井区块矿场试验的最佳注入程序及段塞大小。结果表明,通过对不同浓度的疏水缔合聚合物注入的前置段塞及主段塞和梯度式后续段塞的优化后,原油采收率比未优化时提高了约10个百分点。研究表明,渤海油田应该高度重视聚合物驱的注入方式和段塞优化设计,进一步提高聚合物的技术经济效果。     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

Modelling the impact of energy taxation

Energy taxation in Sweden is complicated and strongly guides and governs district energy production. Consequently, there is a need for methods for accurate calculation and analysis of effects that different energy tax schemes may have on district energy utilities. Here, a practicable method to analyse influence of such governmental policy measures is demonstrated. The Swedish Government has for some years now been working on a reform of energy taxation, and during this process, several interest groups have expressed their own proposals for improving and developing the system of energy taxation. Together with the present system of taxation, four new alternatives, including the proposed directive of the European Commission, are outlined in the paper. In a case study, an analysis is made of how the different tax alternatives may influence the choice of profitable investments and use of energy carriers in a medium‐sized district‐heating utility. The calculations are made with a linear‐programming model framework. By calculating suitable types and sizes of new investments, if any, and the operation of existing and potential plants, total energy costs are minimized. Results of the analysis include the most profitable investments, which fuel should be used, roughly when during a year plants should be in operation, and at what output. In most scenarios, the most profitable measure is to invest in a waste incineration plant. However, a crucial assumption is, with reference to the new Swedish waste disposal act, a significant income from incinerating refuse. Without this income, different tax schemes result in different technical solutions being most profitable. An investment in cogeneration seems possible in only one scenario. It is also found that particular features of some alternatives seem to oppose both main governmental policy goals, and intentions of the district heating company. Copyright © 2002 John Wiley & Sons, Ltd.     

Analysis of an algorithm for solution of conditional optimization problems with linear-fractional objective functions over permutations

A method is considered to solve a conditional optimization problem with a linear-fractional objective function over permutations. The performance of sub algorithms to solve this problem is evaluated. The practical efficiency of the algorithm is analyzed by conducting numerical experiments. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 133–146, July–August 2007.     

Anisotropic mesh adaption by metric‐driven optimization

We describe a Gauss–Seidel algorithm for optimizing a three‐dimensional unstructured grid so as to conform to a given metric. The objective function for the optimization process is based on the maximum value of an elemental residual measuring the distance of any simplex in the grid to the local target metric. We analyse different possible choices for the objective function, and we highlight their relative merits and deficiencies. Alternative strategies for conducting the optimization are compared and contrasted in terms of resulting grid quality and computational costs. Numerical simulations are used for demonstrating the features of the proposed methodology, and for studying some of its characteristics. Copyright © 2004 John Wiley & Sons, Ltd.     

Using internal fibre geometry to improve the performance of pin-loaded holes in composite materials

The anisotropic nature of fibre reinforced composites leads to large stress concentrations around pin-loaded holes through standard weave cloths. Proper understanding of how this anisotropic nature affects the load distribution around holes can be utilised to reduce these concentrations if sufficient thought is given to the internal fibre geometry near to the hole. Such local reinforcements need not be highly complex and can be readily produced without excessive effort, producing significant improvements in performance.     

Response level crossing rate of a linear system excited by a partially specified Gaussian load process

The problem of determining the maximum mean response level crossing rate of a linear system driven by a partially specified Gaussian load process has been considered. The partial specification of the load is given only in terms of its total average energy. The critical input power spectral (PSD) function, which maximizes the mean response level crossing rate, is obtained. The critical input PSD turns out to be highly narrow-banded which fails to capture the erratic nature of the excitation. Consequently, the trade-off curve between the maximum mean response level crossing rate and the maximum disorder in the input process, quantified in terms of its entropy rate, has been generated. The method of Pareto optimization is used to tackle the conflicting objectives of the simultaneous maximization of the mean response level crossing rate and the input entropy rate. The non-linear multi-objective optimization has been carried out using a recently developed multi-criteria genetic algorithm scheme. Illustrative example of determining the critical input of an axially vibrating rod, excited by a partially specified stationary Gaussian load process, has been considered.     

Affine Dependence Classification for Communications Minimization

This paper introduces results on placement and communications minimization for systems of affine recurrence equations. We show how to classify the dependences according to the number and nature of communications they may result in. We give both communication-free conditions and conditions for an efficient use of broadcast or neighbor-to-neighbor communication primitives. Since the dependences of a problem can generally not be all communication-free, we finally introduce a heuristic to globally minimize the communications based on the classification of dependences.