Konrad Swirski

Stochastic immune layer optimizer: efficient tool for optimization of combustion process in a boiler

The article presents a method for optimization of combustion process in a boiler. This solution is based on the artificial immune system. A layered optimization system is used, to minimize CO and NOx emission. This solution is implemented in real power plant. The results from this implementation have been presented. They confirm that presented solution is effective and usable in practice.

New Improvements of Immune Inspired Optimizer SILO

The article presents new improvements of an immune inspired optimization method, used to control a combustion process in a steam generating, coal fired, large scale boiler. Immune Inspired Optimizer SILO is implemented at each of three units of Ostroleka Power Plant (Poland) and at one unit in Newton Power Plant (USA). The results from Newton Power Plant are presented. They confirm that presented solution is effective and usable in practice and it can be treated as a good alternative to MFC controllers. The main goal of this solution is CO and NOx emission minimization.

Optimization of Coal Mill Using an MPC Type Controller

The article presents the question of optimization of a ventilation coal mill on the basis of a predictive optimizing controller with a receding horizon, which is an extension of the standard linear MPC (Model Predictive Control) type controllers. The controller has been realized in a digital version operating with a certain sampling period dependent upon the process dynamics. All calculations of the control rules are performed in one cycle which enables the controller to operate in the running mode. On the basis of a right optimization procedure the controller regulates the correction of settings, which are introduced to classic control structures in a fuzzy control system. The non-linear process model, implemented in the controller, is based on the basis of fuzzy neural networks. This structure enables to design, learn and tune NARMAX type models (Nonlinear Auto Regressive Moving Average with auXiliary input) [1]. The process model uses fuzzy rules, where fuzzy rules figure on the side, which helps to avoid sharp switching between them. The consequences of the rules take the form of differential equations of the linear ARX type models. The use of neural networks ensures a fast and efficient implementation and effective learning and tuning. The problem of control is based in on a periodically performed optimization of the performance index, defined on the basis of the assumed project goals. The aim of the controller operation is to eliminate undesired events occurring during mill operation. Such events are: instability of temperature value of air-dust mix after the mill, excessive fluctuation of air temperature before the mill and positioning of primary and secondary air dampers outside the control range. These goals are realized through appropriate control of the primary air damper and revolving speed of the mill. The implementation carried out of the described controller in a digital automatic control system on 8 ventilation mills of a 360 MW brown coal fired boiler. This article presents the results obtained and a carried out analysis.Copyright

Immune Inspired System for Chemical Process Optimization using the example of a Combustion Process in a Power Boiler

The article presents an optimization method of combustion process in a power boiler. Immune inspired optimizer SILO is used to minimize CO and NOx emission. This solution is implemented in each of three units of Ostroleka Power Plant (Poland) and in the Newton Power Plant (USA). The result from the second SILO implementation in Newton Power Plant is presented. The results confirm that this solution is effective and usable in practice and it can be a good alternative to MPC controllers.

Transition state layer in the immune inspired optimizer

The SILO (Stochastic Immune Layer Optimizer) system is a novel, immune inspired solution for an on-line optimization of a largescale industrial processes. Three layers of optimization algorithm were presented in previous papers. Each layer represents a different strategy of steady state optimization of the process. New layer of the optimization algorithm is presented in this paper. The new Transition State layer is responsible for efficient operation of the optimization system during essential process state transitions. New results from SILO implementation in South Korean power plant are presented. They confirm high efficiency of the SILO optimizer in solving technical problems.

Conditioning of Model Identification Task in Immune Inspired Optimizer SILO

Methods which provide good conditioning of model identification task in immune inspired, steady‐state controller SILO (Stochastic Immune Layer Optimizer) are presented in this paper. These methods are implemented in a model based optimization algorithm. The first method uses a safe model to assure that gains of the process’s model can be estimated. The second method is responsible for elimination of potential linear dependences between columns of observation matrix. Moreover new results from one of SILO implementation in polish power plant are presented. They confirm high efficiency of the presented solution in solving technical problems.

Closed Loop NO x Control and Optimisation Using Neural Networks

Abstract This paper presents real industrial applications of Advanced Control for power plant systems and emission controls. Closed loop NOx control using artificial neural nets and boiler optimisation was applied. Basis knowledge to build an advanced control tool called IVY is also briefly described. Obtained control performance is very high for all presented aspects of the combustion process. The advanced controller provided higher unit efficiency, while simultaneously maintaining environmental and technological constraints.