Intelligent control of the lime kiln process with respect to environmental requirements

Further reducing environmental impacts, such as reduced-sulfur emissions, will be among the major challenges facing the pulp and paper industry in the near future. It will not be easy to further decrease emissions at modern pulp mills because all the major emission sources have already been eliminated. New strategies, such as the prevention of emissions at their source, e.g. by means of improved control of the subsequent processes, will therefore undoubtedly be required in order to conform with the present and also future environmental requirements. An increase in the authorities and public's attention and awareness on environmental issues, together with intensifying interest in artificial intelligence (AI) and intelligent systems, were also prime motivator for this thesis work. The primary objective of the research, which has been carried out as a co-operative effort between academic and industrial parties, has been to lower of the total reduced-sulfur (TRS) emissions from a pulp mill by means of intelligent control techniques. The research was focused on the lime reburning process, which is one of the main sources of the TRS emissions at modern pulp mills. In addition, the environmental requirements for lime kilns have become tighter and even at well-managed mills, the emissions tend periodically to exceed the limits set by the authorities. It has also been widely recognized that control of the rotary kiln used for lime calcination is, in many respects, a demanding task. So far, most of the kilns have therefore been operated without supervisory-level control system. However, there are outstanding economical and the environmental improveme nt potentials associated with improved control. Hence, supervisory-level control of the lime reburning process is undoubtedly a prospective application for intelligent control techniques. In the first phase of the research, a comprehensive study of the operation of the lime reburning process was carried out at one of the major Finnish pulp mills, with special attention paid to the factors affecting the TRS emissions. The results showed that, in addition to the considerable enhancement potential in the performance of the kiln process operation, improved kiln control is also a feasible means to reduce emissions. An overall supervisory-level control schema that takes into account both the environmental and operational requirements, was then designed on the basis of the results of the study. The supervisory-level control system, embedded with a certain degree of intelligence, was then incrementally developed and implemented at the pulp mill. The control structure combines both feedforward (FF) control models and supervisory-level feedback (FB) controllers that are based on the linguistic equation (LE) approach, strengthened with certain capabilities for adaptation and constraint handling. Advanced capabilities and highly developed functionality of the control system were achieved by combining information from different knowledge sources, and by using appropriate techniques to solve each of the recognized problems. On the other hand, the complexity of the lime reburning process was handled by implementing a modular system structure, and by utilizing an incremental system development approach. The results obtained during extended testing periods of the system demonstrate that the proposed control schema can be successfully realized in an industrial environment, and that it provides quantifiable benefits in both the economical and ecological respect. The major benefit from the ecological point of the view was an almost 30 % decrease in the mean of the TRS emissions and a considerable reduction, about 90 %, in the proportion of peak emission periods. The main verified economical benefits were an increase of about 5 % in the long-term production capacity. Improvements in reburned lime quality and enhancements in energy efficiency were also obtained compared to the situation during manual operation.reviewe

Modern approaches to control of a multiple hearth furnace in kaolin production

The aim of this thesis is to improve the overall efficiency of the multiple hearth furnace (MHF) in kaolin calcination by developing control strategies which incorporate machine learning based soft sensors to estimate mineralogy related constraints in the control strategy. The objective of the control strategy is to maximize the capacity of the furnace and minimize energy consumption while maintaining the product quality of the calcined kaolin. First, the description of the process of interest is given, highlighting the control strategy currently implemented at the calciner studied in this work. Next, the state of the art on control of calcination furnaces is presented and discussed. Then, the description of the mechanistic model of the MHF, which plays a key role in the testing environment, is provided and an analysis of the MHF dynamic behavior based on the industrial and simulated data is presented. The design of the mineralogy-driven control strategy for the multiple hearth furnace and its implementation in the simulation environment are also outlined. The analysis of the results is then presented. Furthermore, the extensive sampling campaign for testing the soft sensors and the control strategy logic of the industrial MHF is reported, and the results are analyzed and discussed. Finally, an introduction to Model Predictive Control (MPC) is presented, the design of the Linear MPC framework for the MHF in kaolin calcination is described and discussed, and future research is outlined

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Modeling and Simulation of Metallurgical Processes in Ironmaking and Steelmaking

In recent years, improving the sustainability of the steel industry and reducing its CO2 emissions has become a global focus. To achieve this goal, further process optimization in terms of energy and resource efficiency and the development of new processes and process routes are necessary. Modeling and simulation have established themselves as invaluable sources of information for otherwise unknown process parameters and as an alternative to plant trials that involves lower costs, risks, and time. Models also open up new possibilities for model-based control of metallurgical processes. This Special Issue focuses on recent advances in the modeling and simulation of unit processes in iron and steelmaking. It includes reviews on the fundamentals of modeling and simulation of metallurgical processes, as well as contributions from the areas of iron reduction/ironmaking, steelmaking via the primary and secondary route, and continuous casting

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

Marshall Space Flight Center Research and Technology Report 2019

Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Centerwe make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This years report features 10 of the Agencys 16 Technology Areas, and I am proud of Marshalls role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Centers capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

Enhancing Future Skills and Entrepreneurship

This open access book presents the proceedings of the 3rd Indo-German Conference on Sustainability in Engineering held at Birla Institute of Technology and Science, Pilani, India, on September 16–17, 2019. Intended to foster the synergies between research and education, the conference is one of the joint activities of the BITS Pilani and TU Braunschweig conducted under the auspices of Indo-German Center for Sustainable Manufacturing, established in 2009. The book is divided into three sections: engineering, education and entrepreneurship, covering a range of topics, such as renewable energy forecasting, design & simulation, Industry 4.0, and soft & intelligent sensors for energy efficiency. It also includes case studies on lean and green manufacturing, and life cycle analysis of ceramic products, as well as papers on teaching/learning methods based on the use of learning factories to improve students’problem-solving and personal skills. Moreover, the book discusses high-tech ideas to help the large number of unemployed engineering graduates looking for jobs become tech entrepreneurs. Given its broad scope, it will appeal to academics and industry professionals alike

Chemical machining of advanced ceramics

Not until recently did we see an enormous surge of interest in the study of machining of advanced ceramics. This has resulted in significant advances lately in their development and usage. Machinable glass ceramics, boron nitride and silicon carbide are commonly used in the industry and their major features of attraction are their inherent properties. Previous studies on machining of these materials were mainly performed by other machining methods, such as electrode discharge machining, laser beam machining and abrasive jet machining. Although chemical machining is one of the oldest machining methods employed, the literature survey reveals a lack of knowledge in this particular aspect. Further understanding is required on the chemical machining characteristics of advanced ceramics as well as their performance and relationship between the variables and parameters involved in the process. Therefore, the aim of our study is to examine and establish the relationship between etching rate, surface roughness and dimensional accuracy with the relevant variables involved and at the same time to develop the predictive models for all outputs that we believe are beneficial to the manufacturing industries.A comprehensive review was written and published recently in a Journal on the current advanced ceramics machining techniques [1]. The chemical machining process was successfully conducted in this study with a variety of selected etchants. Using the RSM methodology the first and second order models were developed to study the chemical machining process and relationship between the outputs (etching rate, surface roughness and dimensional accuracy) with the selected variables, namely, etching temperature, etching duration, etchant and etchant’s concentration. A number of predictive models were developed followed by optimisation studies of chemical machining to obtain the best performance of chemical machining of advanced ceramics. Artificial neural network was also used as the analytical tool to evaluate the experimental data and validate the results generated by response surface roughness, and both results were found to be in good agreement with each other. Artificial neural network was performed by software of NeuroSolution 5.From the chemical etching studies both the etching temperature and etchant used have significant influence on the etch rate. Generally, the higher the etching temperature the greater the etch rates was observed for the substrates. The best etch rate was found in HBr etchant for MGC and BN, and the highest etch rate performance for SiC was found in H3PO4 etchant. For surface roughness, different substrates were found to be influenced by different variables. For MGC and BN, these substrates were affected by etching temperature and the best surface roughness occurred at high etching temperature of 90oC. Etching duration was also found to be critical in determining the quality of SiC surface roughness during chemical machining.Experimental data revealed that etching rate was closely correlated to surface roughness as well as the etching ratio. However, using the best etching rate it failed to yield the quality surface roughness, but produced the best etching ratio. Each variable presented different level of significance for each output of chemical machining. The results of etch rate and etch ratio also showed that etching temperature and etching duration imparted significant impact on the chemical machining of all substrates. In the analysis of surface roughness, etching temperature was found to be the critical variable in chemical machining of machinable glass ceramics. Etching temperature and etchant influenced the surface roughness of boron nitride whereas surface roughness of silicon carbide was more dependent on etching duration and etchant used.Predictive models were developed using DE 7 once the analysis of data was completed. A total of 27 predictive models were developed for each substrate and each output. This predictive model can be used directly in the industry with the selected substrate and etchant. Optimisation of chemical machining was also performed. For machinable glass ceramic, the optimum of chemical machining happened at 100oC in 10.5 molarity HCl etchant for 30 minutes. Results of chemical machining of machinable glass ceramics were obtained with optimal etching rate of 0.0008g/min, surface roughness improvement of 81.818nm (48% improvement) and etching ratio of 3.403. In chemical etching of boron nitride, the best result occurred at 40oC in 6 molarity HBr for 62 minutes. The etching rate obtained for BN is 0.00025g/min, with surface roughness improvement of 0.01nm (16% improvement) and etching ratio of 3.153. For the chemical etching of silicon carbide, the best performance occurred at 75oC in 8.5 molarity of HBr for 240 minutes. The optimal value of etching rate for silicon carbide is 0.0009g/min, with surface roughness improvement of 128.71um (35% improvement) and etching ratio of 10.004

NASA Tech Briefs, May 1993

Topics include: Advanced Composites and Plastics; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences