Performance derivative calculations and optimization process

The calculation of the sensitivity versus a geometric or physical parameter and its use in an optimization process are presented. The 2-D finite-element method is used in the magnetic field calculation. The quasiNewton method is used to determine the direction of minimization, and the constraints of upper and lower bounds of parameters are treated by the penalty function. A cubic approximation method is used in the unidimensional minimization. Minimization of the current density (J _c) while keeping the force desired for a relay model is presented as an example     

Selection of optimal process analyzers for plant-wide monitoring

In this paper, the effect of process analyzer selection and positioning on plant-wide process monitoring is investigated. A fundamental problem in process analytical chemistry is the incomparability of different instrument characteristics. A fast but imprecise instrument is incomparable to a slow but precise instrument. Theory is developed to overcome this problem by using an abstract definition of a process analyzer. This definition allows us to put all instrument characteristics for a particular monitoring task on an equal footing. This results in a measurability factor M that expresses monitoring performance of any process measurement by combining instrument characteristics such as precision, sampling rate, grab size, response correlation, and delay time. Both the choice of location and the performance characteristics of different process analyzers can be evaluated using the measurability factor. The unifying nature of the measurability factor allows for a rational decision between completely different process analyzers and locations (Smilde et al., in this issue). The theory is illustrated and validated with an experiment. A tubular reactor for free-radical bulk polymerization of styrene is monitored by in-line short-wave near-infrared spectroscopy at different positions. Alternatively, product samples are collected for at-line near-infrared analysis. Both analyzers measure styrene monomer concentration. The analysis results are used to predict conversion as well as number and weight average molecular mass of the polystyrene reactor product. The theoretical measurability factors for this case study correspond well with the experimental findings.     

Gaussian process regression for multivariate spectroscopic calibration

Traditionally multivariate calibration models have been developed using regression based techniques including principal component regression and partial least squares and their non-linear counterparts. This paper proposes the application of Gaussian process regression as an alternative method for the development of a calibration model. By formulating the regression problem in a probabilistic framework, a Gaussian process is derived from the perspective of Bayesian non-parametric regression, prior to describing its implementation using Markov chain Monte Carlo methods. The flexibility of a Gaussian process, in terms of the parameterization of the covariance function, results in its good performance in terms of the development of a calibration model for both linear and non-linear data sets. To handle the high dimensionality of spectral data, principal component analysis is initially performed on the data, followed by the application of Gaussian process regression to the scores of the extracted principal components. In this sense, the proposed method is a non-linear variant of principal component regression. The effectiveness of the Gaussian process approach for the development of a calibration model is demonstrated through its application to two spectroscopic data sets. A statistical hypothesis test procedure, the paired t-test, is used to undertake an empirical comparison of the Gaussian process approach with conventional calibration techniques, and it is concluded that the Gaussian process exhibits enhanced behaviour.     

Resin transfer molding process optimization

The art of process optimization requires a clear understanding of the differences between each of the widely different optimization strategies available. Often, a sophisticated method that has been well tested in other fields is not applicable at all to problems in resin transfer molding (RTM). This work discusses the strength and weakness of the genetic algorithm and the gradient based algorithms. Two different types of RTM process optimization have been documented. In the first case, gate locations are optimized to minimize the fill time. The optimum design variables were located using the quasi-Newtonian method implemented in the code GLO. In the second case, the permeability of the high permeability layers is varied to minimize resin waste in addition to minimizing the fill time. For this problem, a graphical search was used to locate the optimum design.     

Optimization of near-infrared spectroscopic process monitoring at low signal-to-noise ratio

An approach for the optimization of near-infrared (NIR) spectroscopic process monitoring at low signal-to-noise ratio is presented. It compromises the combined adjustment of different measurement variables and data pretreatments considering the prediction error, economic aspects of the application, and process constraints. The integration time, light intensity, and number of averaged spectra were varied; their mutual influence on the prediction error of partial least squares (PLS) models (i.e., root-mean-square error of cross-validation (RMSECV)) was evaluated in the laboratory. At low signal levels, the spectral uncertainty had a strong impact on the prediction error. It leveled off with increasing values of all three parameters and was finally dominated by other sources of uncertainty. The experimental findings could be characterized and explained by a mathematical equation, which was deduced from theoretical principles. The knowledge about the interaction of the measurement variables allowed their combined adjustment resulting in a reduced impact of spectral uncertainty on the prediction error (i.e., root-mean-square error of prediction (RMSEP)) without additional costs or process modifications. Moreover, a convenient procedure to compensate the stray light caused by strongly absorbing windows was developed. The whole approach was successfully applied to a challenging process, namely, the NIR inline monitoring of the liquid content of two model substances in a rotating suspension dryer.     

Chemometric analysis of Raman spectroscopic data for process control applications

Two examples are given demonstrating the use of multivariate modeling in Raman process control applications. In one example, principal component analysis (PCA) and principal component regression (PCR) are used to model the curing of a high performance thermoset. The PCA results are found to give more accurate results when compared to univariate methods. In a second example, the octane number of gasoline is accurately modeled using partial least squares (PLS) regression analysis. For both examples, methods of normalization are considered in an effort to overcome the limitations of the single beam nature of Raman spectra.     

Automatic semiconductor parameter analyzers

An automatic semiconductor parameter analyzer based on an IBM AT personal computer is described. The results of measurements are used to compile banks of mathematical models of various semiconductor devices (bipolar and field-effect transistors, junction and Zener diodes) for use in circuit simulating programs such as PSpice. Translated from Izmeritel'naya Tekhnika, No. 9, pp. 31–34, September 1998.     

Neural-fuzzy optimization of thick composites curing process

This article addresses the optimization of curing process for thick composite laminates. The proposed methodology aims at the evaluation of the thermal cycle promoting a desired evolution of the degree of cure inside the material. At the same time, temperature overshooting as well as excessive temperature and cure degree gradient through the thickness of the material are prevented. The developed approach is based on the integrated application of artificial neural networks and a fuzzy logic controller. The neural networks promptly predict the behavior of composite material during curing process, while the fuzzy logic controller continuously and opportunely adjusts the proper variations on the imposed thermal cycle. The results highlighted the efficiency of the method in comparison with the cure profiles dictated by the material suppliers. For thick laminates, a reduction of 35% of cure time and improvements of approximately 10% of temperature overshooting was obtained compared to conventional curing cycles. The method was validated by experimental tests.     

An approach to machining process optimization

Optimization is rightly claimed to be the most significant factor distinguishing the modern approach to machining processes from the orthodox one. Practical results can be obtained from the application of mathematical modelling and optimization techniques.

The paper presents a procedure for solution of machining process optimization problems, with the use of theory of graphs and Bellman's optimum principle.

The finite graph G=(N, A) (where -N denoted a set of nodes and A the set of arcs) here presents the set of feasible solutions. Each are represents a corresponding machining operation and each path the feasible machining process.

A number can be associated with each are : the increment of criterion for optimality. Using the optimum principle we can find the shortest possible path in the graph, thus solving given problems of choice for the optimal machining process.

There is a variety of ways in which the performance criterion can be formulated. In this paper only those performance measures having sufficient economical justification are considered. We may minimize the costs of machining processes, or optimize economical effectiveness of capital engaged.

The method presented can be applied with every additive criterion, as well as with some non-additive ones. A practical example illustrates the field of possible application of this method.     

Calibrating optical-absorption gas analyzers

Translated from Izmeritel'naya Tekhnika, No. 6, pp. 52–54, June, 1988.     

TRIZ在管理流程优化中的应用

 TRIZ理论是基于专利分析提出的用于解决发明创造问题的方法论,已经在工程技术领域取得丰硕成果.随着TRIZ研究的不断深入,其基本思想和分析工具已开始在管理领域使用.由于管理问题非结构化的特点,TRIZ工具和解决问题的流程很难直接应用到管理问题中,缺乏TRIZ在管理领域应用的系统化方法.在分析大量文献的基础上,将TRIZ解决问题的一般流程和六西格玛的DMAIC流程作为基础,提出一种TRIZ在管理流程优化中的应用流程——DTSIC （define-translate-solve-improve-control,定义—转化—解决—改进—控制）.并详细分析了每一阶段可以使用的TRIZ方法和工具以及可以结合的管理流程优化工具,给出了一些选择工具的原则.该流程将管理方法与TRIZ原理和工具包有效结合,在不断循环过程中使流程得到改善.     

Development of spectroscopic ellipsometry as in-line control for Co SALICIDE process

This work is aimed at in-line thickness and composition analysis of Co silicides by spectroscopic ellipsometry (SE). The silicides were formed by a two-step rapid thermal annealing (RTA) in nitrogen at different temperatures from initial Co layers deposited on Si (100) substrates and capped by a protective layer of TiN. The optical constants of Co, CoSi and CoSi₂ films were calculated in the wavelength range of 240–800 nm, describing the optical dispersions by harmonic oscillator models. These models were applied for in-line thickness and composition control of the main steps of Co SALICIDE process. The effects of the first RTA temperature and initial Co thickness on formation of silicide phases and their thickness were evaluated. For phase identification, additional methods (sheet resistance, Auger electron spectroscopy and X-ray diffraction) were used. Finally, the suitability of SE for layer thickness uniformity evaluation was demonstrated for the main steps of Co SALICIDE process.     

太阳能吸收式空调性能优化分析

分析影响太阳能两级吸收式空调系统的整体性能的主要因素：集热器进出口热水的温度、各设备中溴化锂溶液的浓度与压力。根据两级吸收式循环的特点，可以运用溶液混合观点构造新型高效的太阳能吸收式制冷空调。运用仿真程序得出了太阳能两级吸收式空调在集热器出口温度在77．5℃左右时可以得到最大的整体效率，为太阳能空调系统的优化奠定了理论基础。     

Multiparametric optimization of the process of isotope separation

We develop a method for determining the optimal parameters and operating conditions of a separating apparatus. We carry out calculations to minimize the specific losses, using as examples the mass-diffusion and thermal-diffusion methods of separation.Translated from Inzhenerno-Fizicheskii Zhurnal, Vol. 42, No. 4, pp. 608–615, April, 1982.     

Automation of polarographs and voltammetric analyzers

We present a review of the state of polarography and voltammetry which will help instrument designers and users to select instruments depending on the object of analysis, the measurement error, the response speed, the registration method, etc. Translated from Izmeritel'naya Tekhnika, No. 7, pp. 65–68, July, 1994.