Stochastic optimization models for energy management in carbonization process of carbon fiber production

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.

Experimental and stochastic approaches to assessing the strain demand of pipelines and flexibility requirements for coatings

A criterion for selecting a coating for an energy pipeline is that the coating should have a suitable flexibility to meet the high strain demand during hydrostatic testing and during field bending. This requires knowledge of the level of strain demand for the pipeline, and also the maximum strain that could be
tolerated by the coating system. Whereas average strains imposed during manufacturing and construction are reasonably well predicted, there is insufficient understanding on the factors leading to localised deformation of the pipe. Significant work has been carried out in the past to develop tests for assessing
the coatings’ ability to handle a certain amount of strain based on bend testing, tensile testing and burst testing. However, there is a concern as to whether these tests properly represent localised micro-strains associated with construction activities including field bending and pressure testing, particularly pressure testing of pipelines designed for operation at 80% of specified minimum yield strength (SMYS). Consequently coatings considered "suitable" for modern pipelines may fail. The first issue discussed in this paper is main factors affecting strain localisation. The non-deterministic distributions of heterogeneities over the pipe provide a ground to consider the mechanisms of localisation as a stochastic process. An approach is proposed to quantify the maximum localised strain demand through cold field bending and hydrostatic experiments. Another issue discussed in this paper is the experimental assessment of coating flexibility under the effects of localised strains. Preliminary mandrel tests have been carried out to assess the uniformity of the imposed strain. Although mandrel testing has been shown to be a useful method for relative comparison of coating flexibility, it has several weaknesses that could significantly affect the reliability and reproducibility of the results.

Is jump risk in iTraxx Sector Indices diversifiable?

The article presents an analysis of jump risks in iTraxx Europe index in a multivariate structural time-series setting for the stochastic process, as well as in the credit default swap (CDS) market. It also examines the rapid development of the credit derivatives market, particularly the CDS market. This analysis found a significant Poisson-distributed jumps in the iTraxx Non-Financials index and its subindices. Based on a statistical analysis, nondiversifiable jump risk strongly exists in the CDS market.

Dynamic analysis of drug-induced cytotoxicity using chip-based dielectrophoretic cell immobilization technology

Quantification of programmed and accidental cell death provides useful end-points for the anticancer drug efficacy assessment. Cell death is, however, a stochastic process. Therefore, the opportunity to dynamically quantify individual cellular states is advantageous over the commonly employed static, end-point assays. In this work, we describe the development and application of a microfabricated, dielectrophoretic (DEP) cell immobilization platform for the realtime analysis of cancer drug-induced cytotoxicity. Microelectrode arrays were designed to generate weak electro-thermal vortices that support efficient drug mixing and rapid cell immobilization at the delta-shape regions of strong electric field formed between the opposite microelectrodes. We applied this technology to the dynamic analysis of hematopoietic tumor cells that represent a particular challenge for real-time imaging due to their dislodgement during image acquisition. The present study was designed to provide a comprehensive mechanistic rationale for accelerated cell-based assays on DEP chips using real-time labeling with cell permeability markers. In this context, we provide data on the complex behavior of viable vs dying cells in the DEP fields and probe the effects of DEP fields upon cell responses to anticancer drugs and overall bioassay performance. Results indicate that simple DEP cell immobilization technology can be readily applied for the dynamic analysis of investigational drugs in hematopoietic cancer cells. This ability is of particular importance in studying the outcome of patient derived cancer cells, when exposed to therapeutic drugs, as these cells are often rare and difficult to collect, purify and immobilize.

Policy recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

Benchmark generation algorithm for stochastic mixed model assembly shop simulation and optimization

The Operations Research (OR) community have defined many deterministic manufacturing control problems mainly focused on scheduling. Well-defined benchmark problems provide a mechanism for communication of the effectiveness of different optimization algorithms. Manufacturing problems within industry are stochastic and complex. Common features of these problems include: variable demand, machine part specific breakdown patterns, part machine specific process durations, continuous production, Finished Goods Inventory (FGI) buffers, bottleneck machines and limited production capacity. Discrete Event Simulation (DES) is a commonly used tool for studying manufacturing systems of realistic complexity. There are few reports of detail-rich benchmark problems for use within the simulation optimization community that are as complex as those faced by production managers. This work details an algorithm that can be used to create single and multistage production control problems. The reported software implementation of the algorithm generates text files in eXtensible Markup Language (XML) format that are easily edited and understood as well as being cross-platform compatible. The distribution and acceptance of benchmark problems generated with the algorithm would enable researchers working on simulation and optimization of manufacturing problems to effectively communicate results to benefit the field in general.

Accuracy and precision assesment of stochastic simulation tools for springback variation

The sheet forming industry is plagued by inherent variations in its many input variables, making quality control and improvements a major hurdle. This is particularly poignant for Advanced High Strength Steels (AHSS), which exhibit a large degree of property variability. Current FE-based simulation packages are successful at predicting the manufacturability of a particular sheet metal components, however, due to their numerical deterministic nature are inherently unable to predict the performance of a real-life production process. Though they are now beginning to incorporate the stochastic nature of production in their codes. This work investigates the accuracy and precision of a current stochastic simulation package, AutoForm Sigma v4.1, by developing an experimental data set where all main sources of variation are captured through precise measurements and standard tensile tests. Using a Dual Phase 600Mpa grade steel a series of semi-cylindrical channels are formed at two Blank Holder Pressure levels where the response metric is the variation in springback determined by the flange angle. The process is replicated in AutoForm Sigma and an assessment of accuracy and precision of the predictions are performed. Results indicate a very good correspondence to the experimental trials, with mean springback response predicted to within 1 ° of the flange angle and the interquartile spread of results to within 0.22°.

Characterising material and process variation effects on springback robustness for a semi-cylindrical sheet metal forming process

Variation in the incoming sheet material and fluctuations in the press setup is unavoidable in many stamping plants. The effect of these variations can have a large influence on the quality of the final stamping, in particular, unpredictable springback of the sheet when the tooling is removed. While stochastic simulation techniques have been developed to simulate this problem, there has been little research that connects the influence of the noise sources to springback. This paper characterises the effect of material and process variation on the robustness of springback for a semi-cylindrical channel forming operation, which shares a similar cross-section profile as many automotive structural components. The study was conducted using the specialised sheet metal forming package AutoForm^TM Sigma, for which a series of stochastic simulations were performed with each of the noise sources incrementally introduced. The effective stress and effective strain scatter in a critical location of the part was examined and a response window, which indicates the respective process robustness, was defined. The incremental introduction of the noise sources allows the change in size of the stressstrain response window to be tracked. The results showed that changes to process variation parameters, such as BHP and friction coefficient, directly affect the strain component of the stressstrain response window by altering the magnitude of external work applied to forming system. Material variation, on the other hand, directly affected the stress component of the response window. A relationship between the effective stressstrain response window and the variation in springback was also established.

A new approach based on support vector machine for solving stochastic optimization

Making decision usually occurs in the state of being uncertain. These kinds of problems often expresses in a formula as optimization problems. It is desire for decision makers to find a solution for optimization problems. Typically, solving optimization problems in uncertain environment is difficult. This paper proposes a new hybrid intelligent algorithm to solve a kind of stochastic optimization i.e. dependent chance programming (DCP) model. In order to speed up the solution process, we used support vector machine regression (SVM regression) to approximate chance functions which is the probability of a sequence of uncertain event occurs based on the training data generated by the stochastic simulation. The proposed algorithm consists of three steps: (1) generate data to estimate the objective function, (2) utilize SVM regression to reveal a trend hidden in the data (3) apply genetic algorithm (GA) based on SVM regression to obtain an estimation for the chance function. Numerical example is presented to show the ability of algorithm in terms of time-consuming and precision.