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Raman analysis can be used to analyse the existence of minerals in an ore sample. Especially the interest here is to analyse given ore sample rapidly, to find out what minerals it contains. Rapid analysis would enable more rapid exploration of minerals as analysis could be carried out on-site. For this study, ore samples were collected from two mines in Northern Finland, Kittila and Kevitsa. An optimisation algorithm was constructed to form a linear combination of reference spectra which best represent the measured spectrum from an ore sample. The reference spectra were collected from a public source. It was found that solving for an optimal summation of reference spectra can be a useful tool in finding out the minerals which are abundant in the sample studied.

The flight control law clearance problem can be formulated as a robustness analysis problem, where a set of suitably defined clearance criteria must be checked to lie within certain limits for all admissible variations of aircraft parameters, pilot inputs and all flight conditions. The idea of optimisation based worst case clearance is to use available and efficient optimisation methods to find those parameters/inputs/flight conditions for which the criteria are violated or poorly satisfied. The potentials of this approach are its general usability for both frequency-domain and time-domain analysis, and for both linear and non-linear models and control laws including protections. Moreover, there is no limitation on the number of parametric uncertainties that can be investigated and the method does not itself add conservatism to the clearance problem as many other alternatives do, mainly those based on approximations or simplifications. The most challenging problem with the optimisation based approach is not to find hidden weaknesses if they exist, but to confidently assert that no weakness exists for all parameters/inputs/flight conditions. To assert this a global optimum solution has to be found usually resulting in a huge amount of computational work. For highly nonlinear clearance problems regarding manoeuvrability in the peripheral flight envelope of a civil transportation aircraft the suitability of the optimisation based approach is demonstrated. Different global optimisation algorithms and strategies are investigated with respect to its reliability in finding worst cases, computational efficiency and its potential for distributed computation in order to reduce time to clearance.

An improved version of the Bees Algorithm is proposed for solving dynamic optimisation problems. This new formulation of the Bees Algorithm includes new search operators, and a new selection procedure that enhances the survival probability of newly formed individuals. The proposed algorithm is tested on six benchmark dynamic optimisation problems. The benchmark problems include minimisation and maximisation tasks of different levels of complexity. For all the problems, the Bees Algorithm finds very satisfactory optima. The very small standard deviation of the results proves the high reliability of the proposed technique. Experimental tests show that the Bees Algorithm outperforms the state-of-the-art Ant Colony Optimisation algorithm. The Bees Algorithm improves also the best results published in the literature for the six benchmark problems.

An impediment to improve the performance of milling machines are vibrations. In order to avoid chatter, on line monitoring of the milling process is proposed in conjunction with stability diagrams. These diagrams are determined using the semi-discretization method. An optimisation algorithm was developed to increase feed rate and spindle speed while the cutting process remains in the stable area.

In this paper, a hybrid global/local optimisation algorithm is developed and applied to the problem of analysing the robustness of a nonlinear flight control law over continuous regions of the flight envelope. The analysis employs a typical nonlinear clearance criterion used by the European aerospace industry together with a simulation model of a high performance aircraft with a full authority control law which is gain scheduled over the flight envelope. The flexibility of global optimisation methods is exploited to allow for clearance of the flight control law over continuous regions of the flight envelope in the presence of multiple continuous variations in key aircraft parameters. Convergence properties of the optimisation algorithm are improved by incorporating local gradient-based optimisation via a hybrid switching scheme.

Alloy design is a challenging multi-objective optimisation problem, which consists of finding the optimal processing parameters and the corresponding chemical compositions to achieve certain pre-defined mechanical properties of steels. In this paper, we combine fuzzy modelling and Particle Swarm Optimisation (PSO) to address the multi-objective optimal alloy design problem. An adaptive weighted PSO algorithm is introduced to improve the performance of the standard PSO. Based on the established impact toughness fuzzy prediction models, the proposed optimisation algorithm has been successfully applied to the optimal design of heat-treated alloy steels. The experimental results have shown that the algorithm can locate the constrained optimal solutions quickly and provide a useful and effective guide for alloy steels design.

This paper presents the results relating to the development and application of evolutionary multi-objective optimisation algorithms to the design of alloy steels using crisp and logic based objective functions. The applied optimisation algorithm aims at determining the optimal heat treatment regime and the required weight percentages for the chemical composites to obtain the pre-defined mechanical properties of steel. During this process, the targeted mechanical properties and the reliability of their predictions are considered simultaneously.

Uncertainty is an inherent characteristic in most industrial processes, and a variety of approaches including sensitivity analysis, stochastic programming and robust optimisation have been proposed to deal with such uncertainty. Uncertainty in Real-Time Optimisation (RTO), particularly making robust decisions under uncertainty in real-time has received little attention. This paper discusses various sources of uncertainty within the closed RTO loop. A method, based on stochastic programming, that explicitly incorporates uncertainty into the RTO problem is presented and allows solution using conventional optimisation algorithms. A gasoline blending example is used to demonstrate the proposed robust RTO approach.

For a given plant or a process there are a number of control configurations that could be implemented. These different configurations can affect the control of the process and its performance significantly. In this paper evaluation and assessment of a number of control configurations of an industrial pilot scale vaporiser is made. A family of Integrated System Optimisation and parameter estimation (ISOPE) algorithms is used for determination of the preferred control configuration

Based on the simulated annealing technique and the constrained form of power system optimisation problems, this paper develops a simulated-annealing-based optimisation algorithm for power system optimisation problems. The algorithm is general and it possesses the ability to determine the global optimum solution. The developed algorithm is demonstrated by its application to the problem of economic dispatch of electric power.

This work presents a systematic procedure for analyzing the interaction of design and control. The mathematical programming framework of process synthesis is used to formulate and solve an optimization problem with multiple objectives involving open-loop controllability measures and economics. A multiobjective optimisation algorithm based on cutting planes is applied to determine the optimal trade-off using partial derivative information from the noninferior solution set. A binary distillation column example illustrates the proposed approach.

This session is devoted to problems of optimal design of automated production lines. The following problems are discussed: modelling approaches which take into account all important design data; analytical methods for performance evaluation for each design solution; cost evaluation of production lines; optimisation algorithms, based on different production line models.

This paper deals with the application of hierarchical structural analysis to the decomposition and control of complex systems. The strength of the relationship between subsets of variables describing the systems is obtained by computation, using information theory concepts, among a set of experimental data. “Good decompositions” are obtained by drawing the interconnexion graph on the basis of the true existing relationships between subsets of variables.These might fail to appear if only coupling between pairs of variables were computed.The algorithms, based on dynamical clustering are presented and proved to converge; computationnal difficulties are discussed.This approach makes it possible to use partial identification methods, decentralized control, hierarchical optimisation algorithms which converge faster as the interconnexion between subsystems get weaker.

A model of an overhead ore unloader is described. This model has been developed as a laboratory rig and is used to illustrate some aspects of modelling, optimal control and simulation control in an M.Sc. course on the theory and practice of automatic control. The rig is used in conjunction with a digital computer package containing several dynamic optimisation algorithms, including the conjugate gradient method. It has been found that the application is useful as a demonstration in several different lecture courses and also as a one-term (six hours per week for ten weeks) project for two students.