Optimization of Blast Furnace Parameters using Artificial Neural Network

Inside the blast furnace (BF) the process is very complicated and very tough to model mathematically. Blast furnace is the heart of the steel industry as it produces molten pig iron which is the raw material for steel making. It is very important to minimise the operational cost, reduce fuel consumption, and optimise the overall efficiency of the blast furnace and also improve the productivity of the blast furnace. Therefore a multi input multi output (MIMO) artificial neural network (ANN) model has been developed to predict the parameters namely raceway adiabatic flame temperature (RAFT), shaft temperature and uptake temperature. The input parameters in the ANN model are oxygen enrichment, blast volume, blast pressure, top gas pressure, hot blast temperature (HBT), steam injection rate, stove cooler inlet temperature, & stove cooler outlet temperature. For the optimisation of the predictive output back propagation ANN model has been introduced. In this present work, Artificial Neural Network (ANN) has been used to predict and optimise the output parameters. All the input data were collected from Rourkela steel plant (RSP) of blast number IV during the one month of operation

Application of dynamic factor modelling to financial contagion

Contagion has been described as the spread of idiosyncratic shocks from one mar ket to another in times of ?nancial turmoil. In this work, contagion has been modelled using a global factor to capture the general market movements and idiosyncratic shocks are used to capture co-movements and volatility spill-over between markets. Many previous studies have used pre-speci?ed turmoil and calm periods to understand when contagion occurs. We introduce time-varying parameters which model the volatility spillover from one country to another. This approach avoids the need to pre-specify particular types of periods using external information. E?cient Bayesian inference can be made using the Kalman ?lter in a forward ?ltering and backward sampling algorithm. The model is applied to market indices for Greece and Spain to understand the e?ect of contagion dur ing the European sovereign debt crisis 2007-2013 (Euro crisis) and examine the volatility spillover between Greece and Spain. Similarly, the volatility spillover from Hong Kong to Singapore during the Asian ?nancial crisis 1997-1998 has also been studied. After a review of the research work in the ?nancial contagion area and of the de?nitions used, we have speci?ed a model based on the work by Dungey et al. (2005) and include a world factor. Time varying parameters are introduced and Bayesian inference and MCMC simulations are used to estimate the parameters. This is followed by work using the Normal Mixture model based on the paper by Kim et al. (1998) where we realised that the volatility parameters results depended ii on the value of the ‘mixture o?set’ parameter. We propose method to overcome the problem of setting the parameter value. In the ?nal chapter, a stochastic volatility model with with heavy tails for the innovations in the volatility spillover is used and results from simulated cases and the market data for the Asian ?nancial crisis and Euro crisis are summarised. Brie?y, the Asian ?nancial crisis periods are identi?ed clearly and agree with results in other published work. For the Euro crisis, the periods of volatility spillover (or ?nancial contagion) are identi?ed too, but for smaller periods of time. We conclude with a summary and outline of further work