REAL TIME PULVERISED COAL FLOW SOFT SENSOR FOR THERMAL POWER PLANTS USING EVOLUTIONARY COMPUTATION TECHNIQUES

Pulverised coal preparation system (Coal mills) is the heart of coal-fired power plants. The complex nature of a milling process, together with the complex interactions between coal quality and mill conditions, would lead to immense difficulties for obtaining an effective mathematical model of the milling process. In this paper, vertical spindle coal mills (bowl mill) that are widely used in coal-fired power plants, is considered for the model development and its pulverised fuel flow rate is computed using the model. For the steady state coal mill model development, plant measurements such as air-flow rate, differential pressure across mill etc., are considered as inputs/outputs. The mathematical model is derived from analysis of energy, heat and mass balances. An Evolutionary computation technique is adopted to identify the unknown model parameters using on-line plant data. Validation results indicate that this model is accurate enough to represent the whole process of steady state coal mill dynamics. This coal mill model is being implemented on-line in a 210 MW thermal power plant and the results obtained are compared with plant data. The model is found accurate and robust that will work better in power plants for system monitoring. Therefore, the model can be used for online monitoring, fault detection, and control to improve the efficiency of combustion

Artificial Neurogenesis: An Introduction and Selective Review

International audienceIn this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development— the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this " meta-design " for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations ; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchange-ability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature