An Edge-Cloud based Reference Architecture to support cognitive solutions in Process Industry

Process Industry is one of the leading sectors of the world economy, characterized however by intense environmental impact, and very high-energy consumption. Despite a traditional low innovation pace in PI, in the recent years a strong push at worldwide level towards the dual objective of improving the efficiency of plants and the quality of products, significantly reducing the consumption of electricity and CO2 emissions has taken momentum. Digital Technologies (namely Smart Embedded Systems, IoT, Data, AI and Edge-to-Cloud Technologies) are enabling drivers for a Twin Digital-Green Transition, as well as foundations for human centric, safe, comfortable and inclusive workplaces. Currently, digital sensors in plants produce a large amount of data, which in most cases constitutes just a potential and not a real value for Process Industry, often locked-in in close proprietary systems and seldomly exploited. Digital technologies, with process modelling-simulation via digital twins, can build a bridge between the physical and the virtual worlds, bringing innovation with great efficiency and drastic reduction of waste. In accordance with the guidelines of Industrie 4.0 this work proposes a modular and scalable Reference Architecture, based on open source software, which can be implemented both in brownfield and greenfield scenarios. The ability to distribute processing between the edge, where the data have been created, and the cloud, where the greatest computational resources are available, facilitates the development of integrated digital solutions with cognitive capabilities. The reference architecture is being validated in the three pilot plants, paving the way to the development of integrated planning solutions, with scheduling and control of the plants, optimizing the efficiency and reliability of the supply chain, and balancing energy efficiency

Highly Abrasion-resistant and Long-lasting Concrete

Studded tire usage in Alaska contributes to rutting damage on pavements resulting in high maintenance costs and safety issues. In this study binary, ternary, and quaternary highly-abrasion resistant concrete mix designs, using supplementary cementitious materials (SCMs), were developed. The fresh, mechanical and durability properties of these mix designs were then tested to determine an optimum highly-abrasion resistant concrete mix that could be placed in cold climates to reduce rutting damage. SCMs used included silica fume, ground granulated blast furnace slag, and type F fly ash. Tests conducted measured workability, air content, drying shrinkage, compressive strength, flexural strength, and chloride ion permeability. Resistance to freeze-thaw cycles, scaling due to deicers, and abrasion resistance were also measured. A survey and literature review on concrete pavement practices in Alaska and other cold climates was also conducted. A preliminary construction cost analysis comparing the concrete mix designs developed was also completed

Attributing the Electrical Conductivity into Asphalt Composite Using Kaoline-Dominant Mixture Clays

تمتلك مخاليط الإسفلت إمكانات كبيرة كموصلات كهربائية للعديد من الوظائف في مجال الاستشعار الذاتي والاستشفاء وجمع الطاقة. تتحكم التوصيلية الكهربائية (EC) في صب الخرسانة الإسفلتية إلى مرحلة ذات أولوية لمثل هذه العروض المقدمة، مع مراعاة بعض الإضافات التي تقلل من تلف المواد الأساسية مع مرور الوقت. تستخدم اختبارات الموصلية الليفية سابقًا في توصيل خليط الإسفلت. هناك الآن حاجة للتخفيف بسبب التغيير المفاجئ في منحنى ER المقاومة الكهربائية لظروف الترشيح للمخاليط الاسفلتية، مع مراعاة الانتقال المفاجئ من قيم المقاومة الكهربائية (ER) إلى مرحلة التوصيل الكهربائي (EC). يعتبر وجود الخلائط الطينية الفعالة لتخفيف عيوب التغيرات المفاجئة صديقة للبيئة. لذلك، تبحث هذه الدراسة عن ناتج حيث كفاءة التغليف المستخدمة عن طريق توجيه التوصيل الكهربائي (EC) من الخرسانة الإسفلتية فقط عن طريق إضافة محتوى معين من محتوى البنتونيت في خليط الكاولين المهيمن بين الكولين-البنتونايت0.4 الى الكولين– البنتونايت 0.1 (KB0.1) إلى و’ و(0.4 الى (الكولين-البنتونايت (الكولين-البنتونايت 0.1)   التي تتميز بخصائص جيوفيزيائية مختلفة ومعلما (0.4 الى (الكولين-البنتونايت (الكولين-البنتونايت 0.1) لذلك، فان ثمانية أنواع المختبرة والمختلفة من خليط الطين جيوتقنية مختلفة. يتم تقييم قيم ER بواسطة عينات مختلطة من الأسفلت التي تحتوي على محتويات مختلفة من خليط الطين المهيمن..Asphalt mixtures have great potentials as electrical connectors for many functions in the field of self-sensing, hospitalization and energy collection. Electrical conductivity (EC) controls the pouring of asphalt concrete to a priority stage for such presentations, taking into account some additives that minimize damage to basic materials over time. Previously fibre conductivity tests are used in the conductivity of the asphalt mixture. There is now a need for mitigation due to the sudden change in the ER electrical curve of the filtration conditions of the down mixtures, considering the sudden transition from the electrical resistivity (ER) values ​​to the conduction phase (EC). Efficient clay mixtures to relieve defects of sudden changes are considered eco-friendly. Therefore, this study looks for an output where encapsulation efficiency used by directing the electrical conductivity (EC) of asphalt concrete only by adding a specific content of bentonite content in the kaolin-dominant mixture between (KB0.1) to (KB0.4). Therefore, eight different types of clay mixture KB0.1 to KB0.4 and Kaolinite 0.9-Sand 0.1 to KS0.4 have different geophysical properties and different geotechnical parameters. EC values. ER values are evaluated by asphalt-mixed samples containing different contents of the dominant K-clay mixture

Prediction of rutting potential of dense bituminous mixtures with polypropylene fibers via repeated creep testing by using neuro-fuzzy approach

This study investigates the potential use of the neuro-fuzzy (NF) approach to model the rutting prediction by the aid of repeated creep testing results for polypropylene modified asphalt mixtures. Marshall specimens, fabricated with M-03 type polypropylene fibers at optimum bitumen content have been tested in order to predict their rutting potential under different load values and loading patterns at 50°C. Throughout the testing phase, it has been clearly shown that the addition of polypropylene fibers results in improved Marshall stabilities and decrease in the flow values, providing an eminent increase of the service life of samples under repeated creep testing. The performance of the accuracy of proposed neuro-fuzzy model is observed to be quite satisfactory. In addition, to obtain the main effects plot, a wide range of detailed two and three dimensional parametric studies have been performed

Artificial Intelligence-Based Prediction of Permeable Pavement Surface Infiltration Rates

Permeable pavements are a type of low impact development technology that is an alternative to conventional asphalt pavements. These pavements are used to address urban stormwater runoff concerns through infiltration and storage. Overtime, sediments carried by stormwater runoff degrade the performance of these pavements and can eventually diminish the infiltration capacity to the point where no infiltration takes place. The objective of this research is to develop a data-driven model to predict the infiltration rate of permeable pavements. Four permeable concrete lab specimens were constructed and subjected to clogging cycles while obtaining surface images and infiltration data. An artificial neural network was created to investigate the relationship between the images of the pavement surface and its associated surface infiltration rate. Results indicated that image parameters do change significantly as pavements clog and are suitable as inputs to predict surface infiltration rate, although model variability needs to be addressed

Particle Dynamics Simulation toward High-Shear Mixing Process in Many Particle Systems

Granular materials appear in a broad range of industrial processes, including mineral processing, plastics manufacturing, ceramic component, pharmaceutical tablets and food products. Engineers and scientists are always seeking efficient tools that can characterize, predict, or simulate the effective material properties in a timely manner and with acceptable accuracy, such that the cost for design and develop novel composite granular materials could be reduced. The major scope of this dissertation covers the development, verification and validation of particle system simulations, including solid-liquid two-phase particle mixing process and foaming asphalt process. High shear mixing process is investigated in detail with different types of mixers. Besides particle mixing study, one liquid-gas two phase foaming asphalt simulation is studied to show the broad capacity of our particulate dynamics simulation scheme. Methodologies and numerical studies for different scenarios are presented, and acceleration plans to speed up the simulations are discussed in detail. The dissertation starts with the problem statement, which briefly demonstrates the background of the problem and introduces the numerical models built from the physical world. In this work, liquid-solid two-phase particle mixing process is mainly studied. These mixing processes are conducted in a sealed mixer and different types of particles are mixed with the rotation of the mixer blades, to obtain a homogeneous particle mixture. In addition to the solid-liquid particle mixing problem, foaming asphalt problem, which is a liquid-gas two phase flow problem is also investigated. Foaming asphalt is generated by injecting a small amount of liquid additive (usually water) to asphalt at a high temperature. The volume change during this asphalt foaming process is studied. Given the problem statement, detailed methodologies of particle dynamics simulation are illustrated. For solid-liquid particle mixing, Smoothed Particle Hydrodynamics (SPH) and Discrete Element Method (DEM) are introduced and implemented to simulate the dynamics of solid and liquid particles, respectively. Solid-liquid particle interactions are computed according to Darcy`s Law. Then the proposed SPH coupling DEM model is verified by three classical case studies. For foaming asphalt problems, a SPH numerical model for foaming asphalt simulation is proposed, and simulations with different water contents, pressures and temperatures are conducted and the results agree with the experiments well. The coupled SPH-DEM method is applied to the particle mixing process, and several particle mixing numerical studies are conducted and these simulations are analyzed in multiple aspects. For the solid-liquid particle mixing problem, liquid plays an important role in the mixing performance. The effects of liquid content and liquid viscosity on mixing performance are studied. The mixing indexes of the mixture are applied to analyze the mixing quality, and the differences between three kinds of mixing indexes are discussed. Then mixers commonly used in industry such as Double Planetary Mixer (DPM) are modeled in mixing simulation and their results are compared with the experiments. Similar to other numerical simulation problems, the scale of the model and the accuracy of the simulation results are constrained by the computational capacity. Our in-house software package Particle Dynamics Parallel Simulator(PDPS) has been used as a platform to implement the algorithms above and conduct the simulations. Two parallel computing methods of Message Passing Interface (MPI) parallel computing and Graphics Processing Unit (GPU) acceleration have been used to accelerate the simulations. Speedup results for both MPI parallel computing and GPU methods are illustrated in the case studies. In summary, a comprehensive approach for particle simulation is proposed and applied to particle mixing process and asphalt foaming simulation. The simulation results are analyzed in various aspects to provide valuable insights to the problems studied in this work. Given the improvement of computational capacity, particle dynamics in higher resolution and simulations in more complex configurations can be obtained. This particle simulation platform is general and it can be straightforwardly extended to many-particle systems with more particle phases and solid-liquid-gas dynamics problems

Experimental and informational modeling study on flexural strength of eco-friendly concrete incorporating coal waste

Construction activities have been a primary cause for depleting natural resources and are associated with stern environmental impact. Developing concrete mixture designs that meet project specifications is time-consuming, costly, and requires many trial batches and destructive tests that lead to material wastage. Computational intelligence can offer an eco-friendly alternative with superior accuracy and performance. In this study, coal waste was used as a recycled additive in concrete. The flexural strength of a large number of mixture designs was evaluated to create an experimental database. A hybrid artificial neural network (ANN) coupled with response surface methodology (RSM) was trained and employed to predict the flexural strength of coal waste-treated concrete. In this process, four influential parameters including the cement content, water-to-cement ratio, volume of gravel, and coal waste replacement level were specified as independent input variables. The results show that concrete incorporating 3% recycled coal waste could be a competitive and eco-efficient alternative in construction activities while attaining a superior flexural strength of 6.7 MPa. The RSM-modified ANN achieved superior predictive accuracy with an RMSE of 0.875. Based on the experimental results and model predictions, estimating the flexural strength of concrete incorporating waste coal using the RSM-modified ANN model yielded superior accuracy and can be used in engineering practice to save the effort, cost, and material wastage associated with trial batches and destructive laboratory testing while producing mixtures with enhanced flexural strength