2 research outputs found
Soft sensor development using artificial intelligence and statistical multivariate methods
The lack of real-time measurement of certain critical product and process characteristics is a major problem in the manufacturing industry, and it can lead to an out of specification production. A soft sensor is a predictive model that uses readily available process measurements to infer variables that are impossible or difficult to obtain in real-time. In this work, historical process data related to the black liquor recovery circuit from a Canadian kraft pulp and paper mill is used to develop soft sensor models for the black liquor solid content at the concentrator feed. Prior to modeling, irrelevant variables and observations not representative of a normal operating regime are eliminated from the dataset. For practical reasons related to modeling restrictions and soft sensor industrial implementation, is proposed that a limited number of variables be used as model inputs. Two Partial Least Squares-based selection criteria are used to select the most relevant predictors. Two different sets of ten variables are obtained and used to develop Sugeno-type fuzzy logic, neural network and Partial Least Regression models. Their predictive performance is compared in order to determine the best model configuration and input selection method. iii Currently, the black liquor solid content at the concentrator feed is measured once every eight hours, by performing a laboratory analysis. The proposed soft sensor model can be used to provide a real-time value of the solid content, allowing operators to monitor the process and act timely if corrective actions are required
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Modelling and design of the eco-system of causality for real-time systems
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.The purpose of this research work is to propose an improved method for real-time sensitivity analysis (SA) applicable to large-scale complex systems. Borrowed from the EventTracker principle of the interrelation of causal events, it deploys the Rank Order Clustering (ROC) method to automatically group every relevant system input to parameters that represent the system state (i.e. output). The fundamental principle of event modelling is that the state of a given system is a function of every acquirable piece of knowledge or data (input) of events that occur within the system and its wider operational environment unless proven otherwise. It therefore strives to build the theoretical and practical foundation for the engineering of input data. The event modelling platform proposed attempts to filter unwanted data, and more importantly, include information that was thought to be irrelevant at the outset of the design process. The underpinning logic of the proposed Event Clustering technique (EventiC) is to build causal relationship between the events that trigger the inputs and outputs of the system. EventiC groups inputs with relevant corresponding outputs and measures the impact of each input variable on the output variables in short spans of time (relative real-time). It is believed that this grouping of relevant input-output event data by order of its importance in real-time is the key contribution to knowledge in this subject area. Our motivation is that components of current complex and organised systems are capable of generating and sharing information within their network of interrelated devices and systems. In addition to being an intelligent recorder of events, EventiC could also be a platform for preliminary data and knowledge construction. This improvement in the quality, and at times the quantity of input data, may lead to improved higher level mathematical formalism. It is hoped that better models will translate into superior controls and decision making. It is therefore believed that the projected outcome of this research work can be used to predict, stabilize (control), and optimize (operational research) the work of complex systems in the shortest possible time. For proof of concept, EventiC was designed using the MATLAB package and implemented using real-time data from the monitoring and control system of a typical cement manufacturing plant. The purpose for this deployment was to test and validate the concept, and to demonstrate whether the clusters of input data and their levels of importance against system performance indicators could be approved by industry experts. EventiC was used as an input variable selection tool for improving the existing fuzzy controller of the plant. Finally, EventiC was compared with its predecessor EventTracker using the same case study. The results revealed improvements in both computational efficiency and the quality of input variable selection