A Fuzzy Logic Control application to the Cement Industry

A case study on continuous process control based on fuzzy logic and supported by expert knowledge is proposed. The aim is to control the coal-grinding operations in a cement manufacturing plant. Fuzzy logic is based on linguistic variables that emulate human judgment and can solve complex modeling problems subject to uncertainty or incomplete information. Fuzzy controllers can handle control problems when an accurate model of the process is unavailable, ill-defined, or subject to excessive parameter variations. The system implementation resulted in productivity gains and energy consumption reductions of 3% and 5% respectively, in line with the literature related to similar applications

A human-machine learning curve for stochastic assembly line balancing problems

The Assembly Line Balancing Problem (ALBP) represents one of the most explored research topics in manufacturing. However, only a few contributions have investigated the effect of the combined abilities of humans and machines in order to reach a balancing solution. It is well-recognized that human beings learn to perform assembly tasks over time, with the effect of reducing the time needed for unitary tasks. This implies a need to re-balance assembly lines periodically, in accordance with the increased level of human experience. However, given an assembly task that is partially performed by automatic equipment, it could be argued that some subtasks are not subject to learning effects. Breaking up assembly tasks into human and automatic subtasks represents the first step towards more sophisticated approaches for ALBP. In this paper, a learning curve is introduced that captures this disaggregation, which is then applied to a stochastic ALBP. Finally, a numerical example is proposed to show how this learning curve affects balancing solutions

Estimating the circularity performance of an emerging industrial symbiosis network: The case of recycled plastic fibers in reinforced concrete

In recent times, the construction industry has been handling circular economy strategies in order to face the most important challenges in the sector, namely the lack of raw materials and the environmental impacts derived from all the processes linked to the entire supply chain. The industrial symbiosis approach represents an effective strategy to improve the circularity of the construction industry. This study analyses the circularity performance of an emerging industrial symbiosis network derived from the production of a cement mortar reinforced with recycled synthetic fibers coming from artificial turf carpets. From the collection of artificial turf carpets at the end-of-life stage it is possible to recover several materials, leading to potential unusual interactions between industries belonging to different sectors. A suitable indicator, retrieved from the literature, the Industrial Symbiosis Indicator (ISI), has been used to estimate the level of industrial symbiosis associated with increasing materials recirculation inside the network. Four scenarios—ranging from perfect linearity to perfect circularity—representing growing circularity were tested. Findings demonstrate that the development of an effective industrial symbiosis network can contribute to improving the circular approach within the construction sector, reducing environmental and economic pressures

Quantum measurement in a family of hidden-variable theories

The measurement process for hidden-configuration formulations of quantum mechanics is analysed. It is shown how a satisfactory description of quantum measurement can be given in this framework. The unified treatment of hidden-configuration theories, including Bohmian mechanics and Nelson's stochastic mechanics, helps in understanding the true reasons why the problem of quantum measurement can succesfully be solved within such theories.Comment: 16 pages, LaTeX; all special macros are included in the file; a figure is there, but it is processed by LaTe

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously. In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

Replacing fossil fuels with renewable energy sources is considered as an effective means to reduce carbon emissions at the industrial level and it is often supported by local authorities. However, individual firms still encounter technical and financial barriers that hinder the installation of renewables. The eco-industrial park approach aims to create synergies among firms thereby enabling them to share and efficiently use natural and economic resources. It also provides a suitable model to encourage the use of renewable energy sources in the industry sector. Synergies among eco-industrial parks and the adjacent urban areas can lead to the development of optimized energy production plants, so that the excess energy is available to cover some of the energy demands of nearby towns. This study thus provides an overview of the scientific literature on energy synergies within eco-industrial parks, which facilitate the uptake of renewable energy sources at the industrial level, potentially creating urban-industrial energy symbiosis. The literature analysis was conducted by arranging the energy-related content into thematic categories, aimed at exploring energy symbiosis options within eco-industrial parks. It focuses on the urban-industrial energy symbiosis solutions, in terms of design and optimization models, technologies used and organizational strategies. The study highlights four main pathways to implement energy synergies, and demonstrates viable solutions to improve renewable energy sources uptake at the industrial level. A number of research gaps are also identified, revealing that the energy symbiosis networks between industrial and urban areas integrating renewable energy systems, are under-investigated

Strain development and damage accumulation under ion irradiation of polycrystalline Ge-Sb-Te alloys

The atomic displacement produced by ion irradiation with 150 keV Ar+ ions has been studied in Ge1Sb2Te4 and Ge2Sb2Te5. Electrical, optical and structural measurements have been employed to characterize the induced electrical and structural modifications. At low temperature the amorphization threshold, evaluated by in situ reflectivity measurements, is independent of the composition and the crystalline structure, and it is equal to 1 x 1013 cm-2. At room temperature, at which dynamic annealing can take place, Ge2Sb2Te5 and Ge1Sb2Te4 in the rocksalt phase exhibit the same amorphization threshold (3 x 1013 cm-2). In the trigonal structure, instead, a higher fluence is required to amorphize the Ge1Sb2Te4, compared to Ge2Sb2Te5. The observed differences between the two compositions can be explained considering the effect of dynamic annealing during ion irradiation of the trigonal phase, which is characterized by the presence of van der Waals gaps. These may act as a preferential sink for the diffusion of the displaced atoms and the filling of these gaps tunes the electronic and structural properties. Filling of about 30% of the gaps produces an electronic transition from metallic to insulating behavior. By further increasing the disorder and filling more than 70% of the gaps the films convert into the rocksalt phase

Clinical implications of malnutrition in the management of patients with pancreatic cancer: Introducing the concept of the nutritional oncology board

Pancreatic cancer represents a very challenging disease, with an increasing incidence and an extremely poor prognosis. Peculiar features of this tumor entity are represented by pancreatic exocrine insufficiency and an early and intense nutritional imbalance, leading to the highly prevalent and multifactorial syndrome known as cancer cachexia. Recently, also the concept of sarcopenic obesity has emerged, making the concept of pancreatic cancer malnutrition even more multifaceted and complex. Overall, these nutritional derangements play a pivotal role in contributing to the dismal course of this malignancy. However, their relevance is often underrated and their assessment is rarely applied in clinical daily practice with relevant negative impact for patients’ outcome in neoadjuvant, surgical, and metastatic settings. The proper detection and management of pancreatic cancer-related malnutrition syndromes are of primary importance and deserve a specific and multidisciplinary (clinical nutrition, oncology, etc.) approach to improve survival, but also the quality of life. In this context, the introduction of a “Nutritional Oncology Board” in routine daily practice, aimed at assessing an early systematic screening of patients and at implementing nutritional support from the time of disease diagnosis onward seems to be the right path to take

Role of interfaces on the stability and electrical properties of Ge2Sb2Te5 crystalline structures

GeSbTe-based materials exhibit multiple crystalline phases, from disordered rocksalt, to rocksalt with ordered vacancy layers, and to the stable trigonal phase. In this paper we investigate the role of the interfaces on the structural and electrical properties of Ge2Sb2Te5. We find that the site of nucleation of the metastable rocksalt phase is crucial in determining the evolution towards vacancy ordering and the stable phase. By properly choosing the substrate and the capping layers, nucleation sites engineering can be obtained, thus promoting or preventing the vacancy ordering in the rocksalt structure or the conversion into the trigonal phase. The vacancy ordering occurs at lower annealing temperatures (170 °C) for films deposited in the amorphous phase on silicon (111), compared to the case of SiO2 substrate (200 °C), or in presence of a capping layer (330 °C). The mechanisms governing the nucleation have been explained in terms of interfacial energies. Resistance variations of about one order of magnitude have been measured upon transition from the disordered to the ordered rocksalt structure and then to the trigonal phase. The possibility to control the formation of the crystalline phases characterized by marked resistivity contrast is of fundamental relevance for the development of multilevel phase change data storage