Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies.

Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions

Advancing zero defect manufacturing: A state-of-the-art perspective and future research directions

The aim of this paper is to develop a method based on combination of Empirical Mode Decomposition (EMD) and Hilbert Transform for assessment of power quality events. A distorted waveform can be conceived as superimposition of various oscillating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMF). Hilbert transform is applied to first three IMF to obtain instantaneous amplitude and phase which are then used for constructing feature vector. The work evaluates the detection capability of the methodology and a comparison with S-Transform is made to show the superiority of the technique in detecting the PQ disturbance like voltage spike and notch. A Probabilistic Neural Network is used as a mapping function for identifying the various disturbance classes. Results show a better classification accuracy of the methodology.

Empirical Mode Decomposition with Hilbert Transform for power quality assessment

Clean water is an indispensable essential resource on which humans and other living beings depend. Therefore, the establishment of a water quality prediction model to predict future water quality conditions has a significant social and economic value. In this study, a model based on an artificial neural network (ANN), discrete wavelet transform (DWT), and long short-term memory (LSTM) was constructed to predict the water quality of the Jinjiang River. Firstly, a multi-layer perceptron neural network was used to process the missing values based on the time series in the water quality dataset used in this research. Secondly, the Daubechies 5 (Db5) wavelet was used to divide the water quality data into low-frequency signals and high-frequency signals. Then, the signals were used as the input of LSTM, and LSTM was used for training, testing, and prediction. Finally, the prediction results were compared with the nonlinear auto regression (NAR) neural network model, the ANN-LSTM model, the ARIMA model, multi-layer perceptron neural networks, the LSTM model, and the CNN-LSTM model. The outcome indicated that the ANN-WT-LSTM model proposed in this study performed better than previous models in many evaluation indices. Therefore, the research methods of this study can provide technical support and practical reference for water quality monitoring and the management of the Jinjiang River and other basins.

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A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory

Nitrous oxide (N2O) emissions account for the majority of the carbon footprint of wastewater treatment plants (WWTPs). Many N2O mitigation strategies have since been developed while a holistic view is still missing. This article reviews the state-of-the-art of N2O mitigation studies in wastewater treatment. Through analyzing existing studies, this article presents the essential knowledge to guide N2O mitigations, and the logics behind mitigation strategies. In practice, mitigations are mainly carried out by aeration control, feed scheme optimization, and process optimization. Despite increasingly more studies, real implementation remains rare, which is a combined result of unclear climate change policies/incentives, as well as technical challenges. Five critical technical challenges, as well as opportunities, of N2O mitigations were identified. It is proposed that (i) quantification methods for overall N2O emissions and pathway contributions need improvement; (ii) a reliable while straightforward mathematical model is required to quantify benefits and compare mitigation strategies; (iii) tailored risk assessment needs to be conducted for WWTPs, in which more long-term full-scale trials of N2O mitigation are urgently needed to enable robust assessments of the resulting operational costs and impact on nutrient removal performance; (iv) current mitigation strategies focus on centralized WWTPs, more investigations are warranted for decentralised systems, especially decentralized activated sludge WWTPs; and (v) N2O may be mitigated by adopting novel strategies promoting N2O reduction denitrification or microorganisms that emit less N2O. Overall, we conclude N2O mitigation research is reaching a maturity while challenges still exist for a wider implementation, especially in relation to the reliability of N2O mitigation strategies and potential risks to nutrient removal performances of WWTPs.

Insights into Nitrous Oxide Mitigation Strategies in Wastewater Treatment and Challenges for Wider Implementation

An improved method for the real time sensitivity analysis in large scale complex systems is proposed in this paper. The method borrows principles from the event tracking of interrelated causal events and deploys clustering methods to automatically measure the relevance and contribution made by each input event data (ED) on system outputs. The ethos of the proposed event modeling (EM) technique is that the behavior or the state of a system is a function of the knowledge acquired about events occurring in the system and its wider operational environment. As such it builds on the theoretical and the practical foundation for the engineering of knowledge and data in modern and complex systems. The proposed EM platform EventiC filters noncontributory ED sources and has the potential to include information that was initially thought irrelevant or simply not considered at the design stage. The real-time ability to group and rank relevant input–output ED in order of its importance and relevance will not only improve the data quality, but leads to an improved higher level of mathematical formulization in the modern complex systems. The contribution of the approach to systems’ modeling is in the automation of data analysis, control, and plant process modeling. EventiC has been validated as the monitoring and the control system for a cement factory. In addition to the previously known parameters, the proposed EventiC identified new influential parameters that were previously unknown. It also filtered 18% of the input data without compromising the data quality or the integrity. The solution has improved the quality of input variable selection and simplify plant control strategies.

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EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems

Movement-based brain–computer Interfaces (BCI) rely significantly on the automatic identification of movement intent. They also allow patients with motor disorders to communicate with external devices. The extraction and selection of discriminative characteristics, which often boosts computer complexity, is one of the issues with automatically discovered movement intentions. This research introduces a novel method for automatically categorizing two-class and three-class movement-intention situations utilizing EEG data. In the suggested technique, the raw EEG input is applied directly to a convolutional neural network (CNN) without feature extraction or selection. According to previous research, this is a complex approach. Ten convolutional layers are included in the suggested network design, followed by two fully connected layers. The suggested approach could be employed in BCI applications due to its high accuracy.

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Automatically Identified EEG Signals of Movement Intention Based on CNN Network (End-To-End)

A real-time quality monitoring of the detection and prediction of a defect in fluid dispensing systems is presented. A case study of adhesive placement and dispensing in a semiconductor production system demonstrates the applicability of a combination of PCA to explain the variations in the amount of dispensed fluid syringe needle placement and event-based learning to express the causal relationship between machine and production state with defect types. The resulting definitions of system state and interrelationship of control parameters build the building blocks of Gene Expression Program (GEP) that predicts the formation of droplets and fail or pass product. The results show 99.93 % of accuracy in prediction of defect which is based on the obtained data from glue dispensing model. This integrated solution provides the genetic signature of the glue dispensing process helping to eliminate defects and the adjustment of system state prior to defect formation.

Morad Danishvar

Papers

EventiC: A Real-Time Unbiased Event-Based Learning Technique for Complex Systems

Automatically Identified EEG Signals of Movement Intention Based on CNN Network (End-To-End)

Zero Defect Manufacturing of Microsemiconductors – An Application of Machine Learning and Artificial Intelligence

Classification cardiac beats using arterial blood pressure signal based on discrete wavelet transform and deep convolutional neural network

Assessing circularity of multi-sectoral systems under the Water-Energy-Food-Ecosystems (WEFE) nexus.