Carbon Emission Prediction and Clean Industry Transformation Based on Machine Learning: A Case Study of Sichuan Province

This study preprocessed 2000-2019 energy consumption data for 46 key Sichuan industries using matrix normalization. DBSCAN clustering identified 16 feature classes to objectively group industries. Penalized regression models were then applied for their advantages in overfitting control, high-dimensional data processing, and feature selection - well-suited for the complex energy data. Results showed the second cluster around coal had highest emissions due to production needs. Emissions from gasoline-focused and coke-focused clusters were also significant. Based on this, emission reduction suggestions included clean coal technologies, transportation management, coal-electricity replacement in steel, and industry standardization. The research introduced unsupervised learning to objectively select factors and aimed to explore new emission reduction avenues. In summary, the study identified industry groupings, assessed emissions drivers, and proposed scientific reduction strategies to better inform decision-making using algorithms like DBSCAN and penalized regression models.Comment: 21 pages,19 figure

Induction heating of carbon fibre reinforced polymer composites : Characterization and modelling

Carbon fibre reinforced polymers (CFRP) are lightweight materials with great potential due to their high strength and stiffness relative to their weight. This enables weight reduction in, for example, vehicles, which is important in reducing energy consumption. Their high strength and stiffness along the fibre direction also enable the development of new types of construction parts. The manufacturing of thermoset-based CFRP is often a time-consuming process with relatively low energy efficiency. Common manufacturing methods such as resin transfer moulding, compression moulding, and autoclaving use significantly more energy than is needed to cure the CFRP part. This is because the heat is transferred conductively via the part surface from a tool with a large mass. However, other potential heating methods are available. Due to the electrical conductivity of carbon fibres, it is possible to use induction heating. This means that the heat is generated directly within the CFRP part without the need to heat a tool with a large thermal mass. The idea of using this technique to heat CFRP is not new, but the anisotropy of the material means that it is associated with a higher level of complexity than the induction heating of metals.To make the heat and temperature distribution more predictable, there is a need for better models and knowledge of how the heat is generated and how the temperature is distributed within CFRP during induction heating. In this thesis, different CFRP configurations were characterized and modelled to provide knowledge and methods for predicting the induction heating behaviour of CFRP. The development of the models has resulted in temperature prediction tools, useful for a wide range of fibre volume fractions, and for both woven and cross-ply layups. Methods for characterization of thermal and electrical input parameters to the models were identified and developed. The temperature distributions predicted by the models were proven to be valid

Optimization of machining characteristics during helical milling of AISI D2 steel considering chip geometry

Helical milling is one of the high-performance and high-quality hole manufacturing activities with strong prospects for the automotive and aerospace industries. Literature suggests chip geometry plays a significant role in optimizing machining operations. In the present study, a mechanistic approach is used to estimate the chip geometry, cutting force and power/energy consumption concerning the tool rotation angle. Experiments are conducted at different levels of spindle rotational speed, cutter orbital speed and axial depth of cuts using 8 and 10 mm diameter mill cutters. Experimental results for cutting speed in X, Y and Z directions are measured. A hybrid approach, which combines the Taguchi method and Graph theory and matrix approach (GTMA) technique is used and optimized process parameters. The highest aggregate utility process parameters are met by 2000 rpm spindle speed, 50 rpm orbital speed and 0.2 mm axial cutting depth during helical milling of AISI D2 steel. FEM simulation is used for predicting the chip thickness, cutting forces and power consumption and also validated the optimization

Fourier transform infrared spectroscopy, a powerful tool to monitor biopharmaceuticals production

A Escherichia coli é o microorganismo mais usado como hospedeiro para a produção de produtos recombinantes, tais como plasmídeos usados para terapia génica e vacinação de ADN. Desta forma, torna-se importante compreender as relações metabólicas complexas e a bioprodução de plasmídeo, que ocorre em ambientes de cultura dinâmicos, a fim de controlar e optimizar o desempenho do sistema de expressão recombinante. O objectivo principal deste trabalho consiste em avaliar a potencialidade da espectroscopia FT-IR para monitorizar e caracterizar a produção do plasmídeo pVAX-LacZ em culturas recombinantes de E. coli, nomeadamente para extrair informação relacionada com as variáveis críticas (biomassa, plasmídeo, fontes de carbono e acetato) e informação metabólica da célula hospedeira E. coli. Para tal, culturas de E. coli com diferentes concentrações de glucose e glicerol e diferentes estratégias de cultivo (batch e fed-batch) foram monitorizadas por espectroscopia de infravermelho perto (NIR) e de infravermelho médio (MIR). Tanto a espectroscopia NIR com a MIR permitiram extrair informação sobre as variáveis críticas do bioprocesso, através da construção de modelos de regressão por mínimos quadrados parciais, que resultaram em elevados coeficientes de regressão e baixos erros de previsão. A abordagem NIR apresenta a vantagem de aquisição em tempo real das variáveis do bioprocesso, já a abordagem MIR permite a leitura simultânea de centenas de amostras de várias culturas ao mesmo tempo através do uso multi-microplacas, sendo muito vantajosa nos casos de micro-bioreactores usados para optimização. Para além disso, como os espectros MIR apresentam mais informação do que os espectros NIR, uma vez que representam os modos de vibração fundamentais das biomoléculas, enquanto que os espectros NIR representam sobreposições e combinações de vibrações, os dados espectrais MIR também permitiram a aquisição de informação bioquímica ao longo das culturas de E. coli a partir da análise das componentes principais (PCA) bem como do estudo das características bioquímicas, tais como as reservas de glicogénio e os níveis de transcrição aparente. Portanto, a espectroscopia FT-IR apresenta assim características relevantes para a compreensão e monitorização do processo de produção de culturas recombinantes, sendo, de acordo com Quality-by-Design e Process Analytical Technology, muito importante para fins de controlo e optimização.Escherichia coli is the most used microorganism as host for the production of recombinant products, such as plasmids used for gene therapy and DNA vaccination. Therefore, it is important to understand the complex metabolic relationships and the plasmid bioproduction process occurring in dynamic culture environments, in order to control and optimize the performance of the recombinant expression system. The main goal of this work is to evaluate the potential of Fourier Transform Infrared (FT-IR) spectroscopy to monitor and characterize recombinant E. coli cultures producing the plasmid model pVAX-LacZ, namely to extract information concerning the critical variables (biomass, plasmid, carbon sources and the by-product acetate) and metabolic information regarding the host E. coli. To achieve that cultures of E. coli conducted with different mixture of glucose and glycerol and different cultivation strategies (batch and fed-batch) were monitored in-situ by a fiber optic probe in near- infrared (NIR) and of the cell pellets in at-line in high-throughput mode by mid-infrared (MIR) spectroscopy. Both NIR and MIR spectroscopy setup enabled to extract information regarding the critical variables of the bioprocess by the implementation of partial least square regression models that result in high regression coefficients and low prediction errors. The NIR setup presents the advantage of acquiring in real time the knowledge of the bioprocess variables, where the at-line measurements with the MIR setup presents more advantageous in cases of micro-bioreactors used in optimization protocols, enabling the simultaneously information acquisition of hundreds samples by using multi-microplates. Furthermore, as the MIR spectra presents more information than the NIR spectra, since it represents the fundamental vibration modes of biomolecules while the NIR spectra represents overtones and combinations of vibrations, the MIR data also enabled to acquire biochemical information along the E. coli cultures as pointed out in an principal component analysis and by the estimation of biochemical features as glycogen reserves and apparent transcriptional levels. Therefore, FT-IR spectroscopy presents relevant features towards the understanding and monitoring of the production process of recombinant cultures for control and optimization purposes, in according to the Quality-by-Design and the Process Analytical Technology

Acoustic Characterization and Modeling of Silicone-Bonded Cocoa Crop Waste Using a Model Based on the Gaussian Support Vector Machine

The sustainable management of waste from agricultural crops represents an urgent challenge. One possible solution considers waste as possible secondary raw materials for specific uses. Among these, the use of agricultural waste as a product for the assembly of panels for the sound absorption of living environments represents a particularly suitable solution. In this study, the acoustic properties of the cocoa pod husk were evaluated, using silicone as a binder. Different proportions of materials and thicknesses were evaluated. A Support Vector Machine (SVM)-based model with a Gaussian kernel was then used to predict the acoustic performance of composite materials. The results obtained suggest the adoption of this material for the acoustic correction of living environments and this methodology for the prediction of the acoustic behavior of materials

Virtual Sensor for Fault Detection, Isolation and Data Recovery for Bicomponent Mixing Machine Monitoring

[Abstract] The present research shows the implementation of a virtual sensor for fault detection with the feature of recovering data. The proposal was implemented over a bicomponent mixing machine used for the wind generator blades manufacture based on carbon fiber. The virtual sensor is necessary due to permanent problems with wrong sensor measurements. The solution proposed uses an intelligent model able to predict the sensor measurements, which are compared with the measured value. If this value belongs to a specified range, it is valid. Otherwise, the prediction replaces the read value. The process fault detection feature has been added to the proposal, based on consecutive erroneous readings, obtaining satisfactory results

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

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

Manufacturing System Energy Modeling and Optimization

World energy consumption has continued increasing in recent years. As a major consumer, industrial activities uses about one third of the energy over the last few decades. In the US, automotive manufacturing plants spends millions of dollars on energy. Meanwhile, due to the high energy price and the high correlation between the energy and environment, manufacturers are facing competing pressure from profit, long term brand image, and environmental policies. Thus, it is critical to understand the energy usage and optimize the operation to achieve the best overall objective. This research will establish systematic energy models, forecast energy demands, and optimize the supply systems in manufacturing plants. A combined temporal and organizational framework for manufacturing is studied to drive energy model establishment. Guided by the framework, an automotive manufacturing plant in the post-process phase is used to implement the systematic modeling approach. By comparing with current studies, the systematic approach is shown to be advantageous in terms of amount of information included, feasibility to be applied, ability to identify the potential conservations, and accuracy. This systematic approach also identifies key influential variables for time series analysis. Comparing with traditional time series models, the models informed by manufacturing features are proved to be more accurate in forecasting and more robust to sudden changes. The 16 step-ahead forecast MSE (mean square error) is improved from 16% to 1.54%. In addition, the time series analysis also detects the increasing trend, weekly, and annual seasonality in the energy consumption. Energy demand forecasting is essential to production management and supply stability. Manufacturing plant on-site energy conversion and transmission systems can schedule the optimal strategy according the demand forecasting and optimization criteria. This research shows that the criteria of energy, monetary cost, and environmental emission are three main optimization criteria that are inconsistent in optimal operations. In the studied case, comparing to cost-oriented optimization, energy optimal operation costs 35% more to run the on-site supply system. While the monetary cost optimal operation uses 17% more energy than the energy-oriented operation. Therefore, the research shows that the optimal operation strategy does not only depends on the high/low level energy price and demand, but also relies on decision makers’ preferences. It provides not a point solution to energy use in manufacturing, but instead valuable information for decision making. This research complements the current knowledge gaps in systematic modeling of manufacturing energy use, consumption forecasting, and supply optimization. It increases the understanding of energy usage in the manufacturing system and improves the awareness of the importance of energy conservation and environmental protection

Smart Sensor Monitoring in Machining of Difficult-to-cut Materials

The research activities presented in this thesis are focused on the development of smart sensor monitoring procedures applied to diverse machining processes with particular reference to the machining of difficult-to-cut materials. This work will describe the whole smart sensor monitoring procedure starting from the configuration of the multiple sensor monitoring system for each specific application and proceeding with the methodologies for sensor signal detection and analysis aimed at the extraction of signal features to feed to intelligent decision-making systems based on artificial neural networks. The final aim is to perform tool condition monitoring in advanced machining processes in terms of tool wear diagnosis and forecast, in the perspective of zero defect manufacturing and green technologies. The work has been addressed within the framework of the national MIUR PON research project CAPRI, acronym for “Carrello per atterraggio con attuazione intelligente” (Landing Gear with Intelligent Actuation), and the research project STEP FAR, acronym for “Sviluppo di materiali e Tecnologie Ecocompatibili, di Processi di Foratura, taglio e di Assemblaggio Robotizzato” (Development of eco-compatible materials and technologies for robotised drilling and assembly processes). Both projects are sponsored by DAC, the Campania Technological Aerospace District, and involve two aerospace industries, Magnaghi Aeronautica S.p.A. and Leonardo S.p.A., respectively. Due to the industrial framework in which the projects were developed and taking advantage of the support from the industrial partners, the project activities have been carried out with the aim to contribute to the scientific research in the field of machining process monitoring as well as to promote the industrial applicability of the results. The thesis was structured in order to illustrate all the methodologies, the experimental tests and the results obtained from the research activities. It begins with an introduction to “Sensor monitoring of machining processes” (Chapter 2) with particular attention to the main sensor monitoring applications and the types of sensors which are employed in machining. The key methods for advanced sensor signal processing, including the implementation of sensor fusion technology, are discussed in details as they represent the basic input for cognitive decision-making systems construction. The chapter finally presents a brief discussion on cloud-based manufacturing which will represent one of the future developments of this research work. Chapters 3 and 4 illustrate the case studies of machining process sensor monitoring investigated in the research work. Within the CAPRI project, the feasibility of the dry turning process of Ti6Al4V alloy (Chapter 3) was studied with particular attention to the optimization of the machining parameters avoiding the use of coolant fluids. Since very rapid tool wear is experienced during dry machining of Titanium alloys, the multiple sensor monitoring system was used in order to develop a methodology based on a smart system for on line tool wear detection in terms of maximum flank wear land. Within the STEP FAR project, the drilling process of carbon fibre reinforced (CFRP) composite materials was studied using diverse experimental set-ups. Regarding the tools, three different types of drill bit were employed, including traditional as well as innovative geometry ones. Concerning the investigated materials, two different types of stack configurations were employed, namely CFRP/CFRP stacks and hybrid Al/CFRP stacks. Consequently, the machining parameters for each experimental campaign were varied, and also the methods for signal analysis were changed to verify the performance of the different methodologies. Finally, for each case different neural network configurations were investigated for cognitive-based decision making. First of all, the applicability of the system was tested in order to perform tool wear diagnosis and forecast. Then, the discussion proceeds with a further aim of the research work, which is the reduction of the number of selected sensor signal features, in order to improve the performance of the cognitive decision-making system, simplify modelling and facilitate the implementation of these methodologies in a cloud manufacturing approach to tool condition monitoring. Sensor fusion methodologies were applied to the extracted and selected sensor signal features in the perspective of feature reduction with the purpose to implement these procedures for big data analytics within the Industry 4.0 framework. In conclusion, the positive impact of the proposed tool condition monitoring methodologies based on multiple sensor signal acquisition and processing is illustrated, with particular reference to the reliable assessment of tool state in order to avoid too early or too late cutting tool substitution that negatively affect machining time and cost