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

Complexity measurement in two supply chains with different competitive priorities

Complexity measurement based on the Shannon information entropy is widely used to evaluate variety and uncertainty in supply chains. However, how to use a complexity measurement to support control actions is still an open issue. This article presents a method to calculate the relative complexity, i.e., the relationship between the current and the maximum possible complexity in a Supply Chain. The method relies on unexpected information requirements to mitigate uncertainty. The article studies two real-world Supply Chains of the footwear industry, one competing by cost and quality, the other by flexibility, dependability, and innovation. The second is twice as complex as the first, showing that competitive priorities influence the complexity of the system and that lower complexity does not ensure competitivity

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

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

Observation of confined current ribbon in JET plasmas

we report the identification of a localised current structure inside the JET plasma. It is a field aligned closed helical ribbon, carrying current in the same direction as the background current profile (co-current), rotating toroidally with the ion velocity (co-rotating). It appears to be located at a flat spot in the plasma pressure profile, at the top of the pedestal. The structure appears spontaneously in low density, high rotation plasmas, and can last up to 1.4 s, a time comparable to a local resistive time. It considerably delays the appearance of the first ELM.Comment: 10 pages, 6 figure