Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Monitoring, diagnostics and improvement of process performance

The data generated in a chemical industry is a reflection of the process. With the modern computer control systems and data logging facilities, there is an increasing ability to collect large amounts of data. As there are many underlying aspects of the process in that data, with its proper utilization, it is possible to obtain useful information for process monitoring and fault diagnosis in addition to many other decision making activities. The purpose of this research is to utilize the data driven multivariate techniques of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for the estimation of process parameters. This research also includes analysis and comparison of these techniques for fault detection and diagnosis along with introduction, explanation and results from a new methodology developed in this research work namely Hybrid Independent Component Analysis (HICA).The first part of this research is the utilization of models of PCA and ICA for estimation of process parameters. The individual techniques of PCA and ICA are applied separately to the original data set of a waste water treatment plant (WWTP) and the process parameters for the unknown conditions of the process are calculated. For each of the techniques (PCA and ICA), the validation of the calculated parameters is carried out by construction of Decision Trees on WWTP dataset using inductive data mining and See 5.0. Both individual techniques were able to estimate all parameters successfully. The minor limitation in the validation of all results may be due to the strict application of these techniques to Gaussian and non-Gaussian data sets respectively. Using statistical analysis it was shown that the data set used in this work exhibits Gaussian and non-Gaussian behaviour.In the second part of this work multivariate techniques of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used for fault detection and diagnosis of a process along with introduction of the new technique, Hybrid Independent Component Analysis (HICA). The techniques are applied to two case studies, the waste water treatment plant (WWTP) and an Air pollution data set. As reported in literature, PCA and ICA proved to be useful tools for process monitoring on both data set, but a comparison of PCA and ICA along with the newly developed technique (HICA) illustrated the superiority of HICA over PCA and ICA. It is evident from the fact that PCA detected 74% and 67% of the faults in the WWTP data and Air pollution data set respectively. ICA successfully detected 61.3% and 62% of the faults from these datasets. Finally HICA showed improved results by the detection of 90% and 81% of the faults in both case studies. This showed that the new developed algorithm is more effective than the individual techniques of PCA and ICA. For fault diagnosis using PCA, ICA and HICA, contribution plots are constructed leading to the identification of responsible variable/s for a particular fault. This part also includes the work done for the estimation of process parameters using HICA technique as was done with PCA and ICA in the first part of the research. As expected HICA technique was more successful in estimation of parameters than PCA and ICA in line with its working for process monitoring

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Application of Deep Learning in Chemical Processes: Explainability, Monitoring and Observability

The last decade has seen remarkable advances in speech, image, and language recognition tools that have been made available to the public through computer and mobile devices’ applications. Most of these significant improvements were achieved by Artificial Intelligence (AI)/ deep learning (DL) algorithms (Hinton et al., 2006) that generally refers to a set of novel neural network architectures and algorithms such as long-short term memory (LSTM) units, convolutional networks (CNN), autoencoders (AE), t-distributed stochastic embedding (TSNE), etc. Although neural networks are not new, due to a combination of relatively novel improvements in methods for training the networks and the availability of increasingly powerful computers, one can now model much more complex nonlinear dynamic behaviour by using complex structures of neurons, i.e. more layers of neurons, than ever before (Goodfellow et al., 2016). However, it is recognized that the training of neural nets of such complex structures requires a vast amount of data. In this sense manufacturing processes are good candidates for deep learning applications since they utilize computers and information systems for monitoring and control thus generating a massive amount of data. This is especially true in pharmaceutical companies such as Sanofi Pasteur, the industrial collaborator for the current study, where large data sets are routinely stored for monitoring and regulatory purposes. Although novel DL algorithms have been applied with great success in image analysis, speech recognition, and language translation, their applications to chemical processes and pharmaceutical processes, in particular, are scarce. The current work deals with the investigation of deep learning in process systems engineering for three main areas of application: (i) Developing a deep learning classification model for profit-based operating regions. (ii) Developing both supervised and unsupervised process monitoring algorithms. (iii) Observability Analysis It is recognized that most empirical or black-box models, including DL models, have good generalization capabilities but are difficult to interpret. For example, using these methods it is difficult to understand how a particular decision is made, which input variable/feature is greatly influencing the decision made by the DL models etc. This understanding is expected to shed light on why biased results can be obtained or why a wrong class is predicted with a higher probability in classification problems. Hence, a key goal of the current work is on deriving process insights from DL models. To this end, the work proposes both supervised and unsupervised learning approaches to identify regions of process inputs that result in corresponding regions, i.e. ranges of values, of process profit. Furthermore, it will be shown that the ability to better interpret the model by identifying inputs that are most informative can be used to reduce over-fitting. To this end, a neural network (NN) pruning algorithm is developed that provides important physical insights on the system regarding the inputs that have positive and negative effect on profit function and to detect significant changes in process phenomenon. It is shown that pruning of input variables significantly reduces the number of parameters to be estimated and improves the classification test accuracy for both case studies: the Tennessee Eastman Process (TEP) and an industrial vaccine manufacturing process. The ability to store a large amount of data has permitted the use of deep learning (DL) and optimization algorithms for the process industries. In order to meet high levels of product quality, efficiency, and reliability, a process monitoring system is needed. The two aspects of Statistical Process Control (SPC) are fault detection and diagnosis (FDD). Many multivariate statistical methods like PCA and PLS and their dynamic variants have been extensively used for FD. However, the inherent non-linearities in the process pose challenges while using these linear models. Numerous deep learning FDD approaches have also been developed in the literature. However, the contribution plots for identifying the root cause of the fault have not been derived from Deep Neural Networks (DNNs). To this end, the supervised fault detection problem in the current work is formulated as a binary classification problem while the supervised fault diagnosis problem is formulated as a multi-class classification problem to identify the type of fault. Then, the application of the concept of explainability of DNNs is explored with its particular application in FDD problem. The developed methodology is demonstrated on TEP with non-incipient faults. Incipient faults are faulty conditions where signal to noise ratio is small and have not been widely studied in the literature. To address the same, a hierarchical dynamic deep learning algorithm is developed specifically to address the issue of fault detection and diagnosis of incipient faults. One of the major drawbacks of both the methods described above is the availability of labeled data i.e. normal operation and faulty operation data. From an industrial point of view, most data in an industrial setting, especially for biochemical processes, is obtained during normal operation and faulty data may not be available or may be insufficient. Hence, we also develop an unsupervised DL approach for process monitoring. It involves a novel objective function and a NN architecture that is tailored to detect the faults effectively. The idea is to learn the distribution of normal operation data to differentiate among the fault conditions. In order to demonstrate the advantages of the proposed methodology for fault detection, systematic comparisons are conducted with Multiway Principal Component Analysis (MPCA) and Multiway Partial Least Squares (MPLS) on an industrial scale Penicillin Simulator. Past investigations reported that the variability in productivity in the Sanofi's Pertussis Vaccine Manufacturing process may be highly correlated to biological phenomena, i.e. oxidative stresses, that are not routinely monitored by the company. While the company monitors and stores a large amount of fermentation data it may not be sufficiently informative about the underlying phenomena affecting the level of productivity. Furthermore, since the addition of new sensors in pharmaceutical processes requires extensive and expensive validation and certification procedures, it is very important to assess the potential ability of a sensor to observe relevant phenomena before its actual adoption in the manufacturing environment. This motivates the study of the observability of the phenomena from available data. An algorithm is proposed to check the observability for the classification task from the observed data (measurements). The proposed methodology makes use of a Supervised AE to reduce the dimensionality of the inputs. Thereafter, a criterion on the distance between the samples is used to calculate the percentage of overlap between the defined classes. The proposed algorithm is tested on the benchmark Tennessee Eastman process and then applied to the industrial vaccine manufacturing process

Realising full-scale control in wastewater treatment systems using in situ nutrient sensors

Abstract A major change in paradigm is taking place in the operation of wastewater treatment plants as automatic process control is becoming feasible. This change is due to a number of different reasons, not least the development of online nutrient sensors, which measure the key parameters in the biological nutrient removal processes, i.e. ammonium, nitrate and phosphate. The thesis is about realising full-scale control in wastewater treatment systems using in situ nutrient sensors. The main conclusion of the work is that it is possible to significantly improve the operational performance in full-scale plants by means of relatively simple control structures and controllers based on in situ nutrient sensors. The in situ location should be emphasised as this results in short dead time, hence making simple feedback loops based on proportional and integral actions effective means to control the processes. This conclusion has been reached based on full-scale experiments, where various controllers and control structures for the biological removal of nitrogen and the chemical removal of phosphorous have been tested. The full-scale experiments have shown that it is possible to provide significant savings in energy consumption and precipitation chemicals consumption, reduction in sludge production and improvement of the effluent water quality. The conclusions are supported by model simulations using the COST benchmark simulation platform. The simulations are used for investigating issues regarding the interactions between the main control handles working in the medium time frame (relative gain array analysis). The simulations have also been used for testing various control structures and controllers. Controllers for the following types of control are suggested and tested: „h Control of aeration to obtain a certain effluent ammonium concentration; „h Control of internal recirculation flow rate to obtain maximum inorganic nitrogen removal; „h Control of external carbon dosage together with internal recirculation flow rate to obtain a certain effluent total inorganic nitrogen concentration; „h Optimisation of the choice of sludge age. Additionally, a procedure for implementing new control structures based on nutrient sensor has been proposed. The procedure involves an initial analysis phase, a monitoring phase, an experimenting phase and an automatic process control phase. An international survey with the aim to investigate the correspondence between ICA (instrumentation, control and automation) utilisation and plant performance has been carried out. The survey also gives insight into the current state of ICA applications at wastewater treatment plants