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Incorporating ethics and values within the life cycle of an AI asset means securing its development, deployment, use, and decommission under these perspectives. These approaches depend on the market domain where AI is operational – considering the interaction and the impact on humans if any process does not perform as expected – and the legal compliance, both required to ensure adequate fulfilment of ethics and values. Specifically, in the manufacturing sector, standards were developed since the 1990’s to guarantee, among others, the correct use of mechanical machinery, systems robustness, low product variability, workers safety, system security, and adequate implementation of system constraints. However, it is challenging to blend the existing practices with the needs associated with deployments of AI in a trustworthy manner. This document provides an extended framework for AI Management within the Manufacturing sector. The framework is based on different perspectives related to responsible AI that handle trustworthy issues as risk. The approach is based on the idea that ethical considerations can and should be handled as hazards. If these requirements or constraints are not adequately fulfilled and managed, it is expected severe negative impact on different sustainable pillars. We are proposing a well-structured approach based on risk management that would allow implementing ethical concerns in any life cycle stages of AI components in the manufacturing sector. The framework follows a pipeline structure, with the possibility of being extended and connected with other industrial Risk Management Processes, facilitating its implementation in the manufacturing domain. Furthermore, given the dynamic condition of the regulatory state of AI, the framework allows extension and considerations that could be developed in the future.

Currently, pioneer companies are working hard to construct applied ethical frameworks in different sectors for using AI components that generate trust in their clients and workforce. However, independent of these few companies, there is still a considerable gap between understanding the impact of using responsible AI components, the implications of the lack of use, and what is currently applied in the industrial sector. Given that industry has shown an increased commitment to incorporating AI components, works focus on broadening the understanding of manufacturing sector stakeholders of what approaches could be considered within AI life-cycle, reducing the gap between principles and actionable requirements, and defining fundamental considerations based on risk management for incorporating, and managing, AI-based on responsible AI are required. In this work, we present a summary of the most suitable approaches that can be used for implementation and the lessons learned from a European Funded project (ASSISTANT).

To develop AI-based models capable of governing or providing decision support to complex manufacturing environments, abstractions and mechanisms for unified management of data storage and processing capabilities are needed. Specifically, as such models tend to include and rely on detailed representations of systems, components, and tools with complex interactions, mechanisms for simplifying, integrating, and scaling management capabilities in the presence of complex data requirements (e.g., high volume, velocity, and diversity of data) are of particular interest. A data fabric is a system that provides a unified architecture for management and provisioning of data. In this work we present the background, design requirements, and high-level outline of the ASSISTANT data fabric - a flexible data management tool designed for use in adaptive manufacturing contexts. The paper outlines the implementation of the system with specific focus on the use of domain models and the data modeling approach used, as well as provides a generic use case structure reusable in many industrial contexts.

International audience; The European project ASSISTANT concept will provide a set of AI-based digital twins that helps process engineers and production planners to operate collaborative mixed-model assembly lines based on the data collected from IoT devices and external data sources. Such a tool will help planners to design the assembly line, plan the production, operate the line, and improve process tuning. In addition, the system monitors the line in real-time, ensures that all required resources are available, and allows fast re-planning when necessary. ASSISTANT aims to make cost-effective decisions while ensuring product quality, safety and wellbeing of the workers, and managing the various sources of uncertainties. The resulting digital twin systems will be data-driven, agile, autonomous, collaborative and explainable, safe but reactive.

Vector-born disease models are extensively used for surveillance and control processes. The most simple and generally use model (SEIR-SEI model) cannot explain a variety of phenomena involved in these diseases spread and development. In order to obtain a wider insight of the vector-born disease models (and the dynamics involved in them), this work focuses into analyse the classical model, a modified versions of it, and 8 their parameters. The modified version includes host mobility, 9 environmental, re-susceptibility, and mosquito life cycle considerations. As results it is observed that there are a limiting number of parameters that play the most important roles in the dynamics (those related to mortality rates, recovery rate from infectious, and pathogen transmission probabilities). Therefore, parameters determination should focus primarily into estimate these values. Stronger effects of the environmental variables are observed and expected by using different parameters and/or the use of multiple environmental variable at the same time., Applied Mathematics; Engineering; Computer-Aided Engineering; Ecology; Biological Sciences; Epidemiology; modelling; Vector-Borne Diseases; SEIR-SEI; dengue; Clustering; Sensitivity Analyses

Selection of optimal technologies for novel biobased products and processes is a major challenge in process design, especially when are considered many alternatives available to transform materials into valuable products. Furthermore, such technological alternatives vary in their technical performances and cause different levels of economic and environmental impacts throughout their life cycles. Additionally, selection of optimal production pathways requires a shift from the traditional materials management practices to more sustainable practices. This contribution provides a method for optimizing multi-product network systems from a sustainability perspective by applying the GREENSCOPE framework as a sustainable objective function. A case study is presented in which the four GREENSCOPE target areas (i.e., efficiency, energy, economics, and environment) are evaluated by 21 preselected indicators as part of a multi-objective optimization problem of a biojet fuel production network. The biojet fuel production network evaluated in this study consists of four main elements: (1) feedstocks management, (2) conversion technologies, (3) co-products upgrading, and (4) auxiliary sections for in situ production of raw materials and utilities. For the sustainability objective function, the 21 indicators are analyzed considering multiple perspectives of stakeholders to study their influence on the decision-making process. It is, different sets of weighting factors are assigned to each of the four target areas. Hence, this sustainability evaluation from different stakeholders’ perspectives allows identifying optimal networks, specific target areas with great potential for improvements, and processing steps with great influence in the entire network performance. As a result, diverse optimal network arrangements were obtained according to the multiple stakeholders’ perspectives. This evidences that a win–win situation for all sustainability aspects considered can hardly be reached. Finally, this contribution demonstrated the applicability of the proposed methodology for sustainability evaluation, optimization, and decision-making in the context of a multi-product material facility by developing a multi-objective optimization model.

A microwave hydrothermal treatment was used for the chemical production of lactic acid from alginate extracted from the brown seaweed Padina Durvillaei, collected in the coastal area of Ecuador. The microwave hydrothermal treatment was studied through a response surface methodology based on the Box-Behnken design, using temperature, reaction time, and catalyst concentration as the manipulated variables and the yield as a response factor. The characterization of alginate was performed by Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA). The lactic acid produced was quantified using Gas Chromatography - Ion Mobility Spectrometry (GC-IMS). The results show that temperature and catalyst concentration played the most critical roles in alginate extraction and lactic acid production. The optimal experimental conditions for alginate extraction from brown seaweed were: temperature = 92.91°C, time = 110.81 min and catalyst (Na2CO3) concentration = 2.60%, with a yield = 29.19%. Although the experimental evidence indicates a positive influence of microwaves' use on the production of lactic acid from alginate through hydrothermal treatment, a new study considering temperatures above 220°C and reaction times below 60 min should be developed.

Las Simulaciones de Equilibrio Líquido Vapor (VLE) son ampliamente utilizadas dado su impacto en el escalamiento, diseño y extrapolación de diferentes operaciones unitarias. Sin embargo, dado considerable factores, es casi imposible experimentalmente estudiar cada uno de los sistemas de VLE. La simulación de VLE puede ser desarrollada utilizando representaciones que son fuertemente dependientes de la naturaleza e interacción de los compuestos que conforman la mezcla. Un modelo que ayude en la predicción de esas interacciones facilitará el proceso de simulación. Una Red Neuronal Gris (GNM) fue creada como un predictor de parámetros de interacción binaria, los que son estimados utilizando variables de estado e información de componentes puros. Esta información fue utilizada para predecir el comportamiento de VLE en mezclas y rangos no utilizados en la formulación matemática. Las capacidades predictivas del GNM (incluida la dependencia de temperatura) mostraron errores menores al 5% y al 20% para mezclas consideradas y no consideradas en los datos de entrenamiento, respectivamente.

Continual growing demand for metals in regular and emerging markets has led to an increasing use of chemicals and reagents in ore processing. This trend force to incur in an increasing use of commodities which inevitable leads to higher operational costs and environmental concern. The chemicals and reagents used in flotation processes especially invoke high costs of handling and disposal due to their hazardous nature, but until now, few studies have been carried out to seek possible alternatives. In order to develop a cheaper and greener processes, these hazardous materials should be replaced by more sustainable products, by-products, or wastes generated by other industries. Biosolids, cheaper and greener than chemical frothers and collectors, have been tested successfully in flotation processes. Studies of removal rates and froth flotation kinetics have been carried out independently, nevertheless a deeper understanding of the tradeoffs involved between the rates and kinetics should be obtained. This work evaluates the use of different collectors (conventional collector (CC), biosolids (and their main components), and mixtures of CC and biosolids main components) in the froth flotation of copper sulfide ores. Tests were carried out in Denver Cells, at fixed collector, frother, and pH levels, in order to estimate metallurgical and kinetic parameters. In rougher flotation tests, biosolids show to be the most efficient non-CCs, achieving Cu recoveries of 64.1%. CC achieved 76.2% Cu recoveries while none of the pure biosolids main components achieved Cu recoveries over 60%. In the kinetics studies, only the partial replacement of CC (by 50% of Humic Acids (HAs) or biosolids) allowed a similar copper recovery once compared with CC (~81% Cu to be obtained with a fast kinetic constant of ~0.88 min-1). For molybdenum, partial replacement of CC produced better recovery and kinetics constants (k of 0.83 min-1 and R∞ of 66.10% for 50% CC - 50% HAs; k of 0.90 min-1 and R∞ of 61.79% for 50% CC - 50% biosolids). Results show that different combinations of biosolids - CC or HAs - CC could achieve optimal flotation conditions. As evaluated, an optimal combinations would allow considerable reductions in energy and chemical consumption.

Se pretende añadir valor agregado a las algas pardas (Pheophyta/Padina) ecuatorianas empleándolas como recursos para la producción de ácidos orgánicos y alginato de sodio de manera que se evidencie una fuente alternativa de materia prima en la petroquímica. Basado en el trabajo de Mazumder se determinaron las condiciones para la extracción alginato de sodio empleando agua de mar y concentración de catalizador (Na2CO3) al 0% ,1.5% y 3%; el valor del mejor rendimiento fue de 27,34%. Así como también para la obtención de ácidos orgánicos (ácido láctico y acético) basado en el trabajo de Jeon, se empleó metodología de superficie de respuesta (MSR); para lo cual se empleó agua de mar para dos rutas: con alginato de sodio procedente de Sigma Aldrich y con algas pardas ecuatorianas pre-tratadas. El diseño experimental de Box Behken tiene como factores a la temperatura, tiempo de digestión, salinidad del agua y cantidad de catalizador (CaO). El mayor rendimiento obtenido en el estudio para ácido láctico y acético fue del 30,54% y 5,872% respectivamente. Los productos extraídos se analizaron mediante: Espectroscopia infrarroja con transformada de Fourier (IFTR-ATR), Análisis Termogravimétrico (TGA) y el Escaneo Calorimétrico Diferencial (DSC), para el alginato de sodio; así como para los ácidos orgánicos, Espectrofotometría de Movilidad Iónica (GC-IMS).

Se determinaron las mejores condiciones para la extracción de alginato de sodio a partir del alga parda (Padina durvillaei) y su transformación hidrotérmica a ácido láctico, la cual fue analizada mediante superficie de respuesta Box-Behnken. Las variables evaluadas para la extracción del alginato de sodio a partir del alga fueron: temperatura, tiempo de reacción y concentración alcalina, donde la variable de respuesta fue el rendimiento de alginato. La producción de ácido láctico a partir de alginato y alga se estudió considerando las variables: temperatura, tiempo de reacción y masa de catalizador. La identificación de los grupos funcionales del alginato y la evaluación de la razón ácido manurónico/ácido gulurónico (M/G) se realizó con Espectroscopia Infrarroja de Transformada de Fourier (FTIR). El peso molecular del alginato se determinó con viscosimetría capilar y el comportamiento térmico con Calorimetría Diferencial de Barrido (DSC) y Análisis Termogravimétrico (TGA). El ácido láctico fue cuantificado directamente en el producto final de la reacción por Cromatografía de Gases acoplado a Espectrometría de Movilidad Iónica (GC-IMS). De acuerdo a los resultados obtenidos a partir de la biomasa marina se puede extraer diversos compuestos orgánicos de alto valor agregado, debido a la despolimerización de su estructura en el proceso hidrotermal.

This article presents an intelligent system for container stacking based on fuzzy logic. The method establishes a defined criterion for accepting or rejecting in real time an entry request to the stacking areas of the port in Valparaiso, Chile. A case study based on expert knowledge illustrates the proposed method with real data. First, the optimum solution is determined for a problem of maximization of entries, based on historical records from the traffic and information center of Valparaiso Port. Second, this solution is used to establish a strategy for making “the best possible decisions.” The combination of the optimization and the fuzzy results (which consider the type of cargo, prices, and capacity) is performed at two levels. First, the optimization results are used as feed for the fuzzy system to determinate a ratio of future acceptances. Second, the optimization results are compared to the fuzzy system results in order to estimate a parameter to establish the minimal percentage value for accepting a request. As a result, a proper use of the stacking area is achieved, which results in an increase of profits and revenue management.

Static headspace gas chromatography-ion mobility spectrometry (SHS GC-IMS) is a relatively new analytical technique that has considerable potential for analysis of volatile organic compounds (VOCs). In this study, SHS GC-IMS was used for the identification of the major terpene components of various essential oils (EOs). Based on the data obtained from 25 terpene standards and 50 EOs, a database for fingerprint identification of characteristic terpenes and EOs was generated utilizing SHS GC-IMS for authenticity testing of fragrances in foods, cosmetics, and personal care products. This database contains specific normalized IMS drift times and GC retention indices for 50 terpene components of EOs. Initially, the SHS GC-IMS parameters, e.g., drift gas and carrier gas flow rates, drift tube, and column temperatures, were evaluated to determine suitable operating conditions for terpene separation and identification. Gas chromatography-mass spectrometry (GC-MS) was used as a reference method for the identification of terpenes in EOs. The fingerprint pattern based on the normalized IMS drift times and retention indices of 50 terpenes is presented for 50 EOs. The applicability of the method was proven on examples of ten commercially available food, cosmetic, and personal care product samples. The results confirm the suitability of SHS GC-IMS as a powerful analytical technique for direct identification of terpene components in solid and liquid samples without any pretreatment. Graphical abstract Fingerprint pattern identification of terpenes and essential oils using static headspace gas chromatography-ion mobility spectrometry.

The productivity of the mining industry in Chile, currently the main driver of Chilean economy, is closely tied to foreign demand for ores. Ore-processing is known for involving energy–intensive processes, such as comminution, concentrating and cathodic processes. As mining activities take place in the arid north of Chile, they are affected by water scarcity. Water shortage has forced the industry to pump desalinated seawater up to mining sites over 2000 m above sea level, further increasing electricity consumption. Given these energy needs, and the fact that electrical energy supply in the north of Chile is based on fossil fuels, it is important to consider the use of renewable energies as environment-friendly and economic alternatives. The aim of this work is to evaluate, by an optimized techno-economic analysis, the use of photovoltaic and a novel wind-based technology to supply at least 10% of the current and the predicted electrical energy requirements of the mining industry in the Antofagasta region. A combination of an optimization problem and technical evaluation was performed using Matlab to obtain the optimal number of solar and wind-based technology units in a case study. Total energy generation from a novel wind-based technology unit is 67,616 MWh/y, corresponding to 14.45·10 6 Nm 3 (1298 t) of hydrogen and 7.41·10 6 Nm 3 (10,323 t) of oxygen after electrolytical transformation. Considering a 65% efficiency of the combined cycle fed with hydrogen and oxygen, 28,133 MWh/y of electrical energy would be obtained. For the cases studied the cost of energy from the combined system was estimated to be between 0.255 US$/kWh and 0.273 US$/kWh, slightly higher than the average energy regional cost. According to the analysis, the renewable energy system could be a sustainable alternative to supply economic green energy to the mining industry in Chile.

The application of a Grey-box Neural Model (GNM) in a nonlinear model predictive control scheme (NMPC) of a direct rotary dyer is presented in this work. The GNM, which is based on the combination of phenomenological models and empirical artificial neural network (ANN) models, was properly developed and validated by using experimental fish-meal rotary drying information. The GNM was created by combining the rotary dryer mass and energy balances and a feed forward neural network (FFNN), trained off-line to estimate the drying rate and the volumetric heat transfer coefficient. The GNM results allowed us to obtain the relation between the controlled variable (solid moisture content) and the manipulated variable (gas phase entrance temperature) used in the predictive control strategy. Two NMPC control strategies, one with a fixed extended prediction horizon and another with an extended range prediction horizon, were applied to a simulated industrial fish-meal drying process. The results showed that a correct ...

Tunable solvent systems couple homogeneous catalytic reactions to heterogeneous separations, thereby combining multiple unit operations into a single step and subsequently reducing waste generation and improving process economics. In addition, tunable solvents can require less energy than traditional separations, such as distillation. We extend the impact of such solvents by reporting on the application of two previously described carbon dioxide tunable solvent systems: polyethylene glycol (PEG)/organic tunable solvents (POTS) and organic/aqueous tunable solvents (OATS). In particular, we studied: (1) the palladium catalyzed carbon-oxygen coupling of 1-bromo-3,5-dimethylbenzene and o-cresol to potassium hydroxide to produce o-tolyl-3,5-xylyl ether and 1-bromo-3,5-di-tert-butylbenzene to potassium hydroxide to produce 3,5-di-tert-butylphenol in PEG400/1,4-dioxane/water and (2) the rhodium-catalyzed hydroformylation of p-methylstyrene in water/acetonitrile to form 2-(p-tolyl) propanal. In addition, we introduce a novel tunable solvent system based on a modified OATS where propane replaces carbon dioxide. This represents the first use of propane in a tunable solvent system.

Fluid-phase equilibria, including dew points, bubble points, and critical points were measured for four binary systems composed of a chlorosilane and carbon dioxide. The measurements were carried out in a constant-composition, variable-volume cell equipped with a sapphire window, which allowed visual observation of the phases in the cell. A syringe pump was used to inject the CO2 into the cell and to control its pressure. Methyltrichlorosilane, dimethyldichlorosilane, trimethylchlorosilane, and diethyldichlorosilane up to about 0.14 mol fraction were studied in this apparatus and a total of 243 phase-boundary points were obtained. Displacements in the critical point with respect to pure CO2 of up to 11.81 MPa and 348.05 K were observed. Modeling of the fluid-phase equilibria for three of the four binary systems was done using the Peng–Robinson equation of state, standard van der Waals mixing rules with two binary interaction parameters, and a φ–φ formulation of the equilibrium. The binary interaction parameters were obtained by fitting the model to the experimental data. The model produced excellent agreement between computed and experimental data. Graphical representations of the modeling results are presented and compared to experimental results. The results indicate that the largest chlorosilane (diethyldichlorosilane) produced the largest shift in critical pressure and critical temperature with respect to pure CO2.

Zeta potential measurements were used to assess the electrokinetic characteristics of chalcopyrite, molybdenite and pyrite in the presence of biosolids and their main components (humic acids, glucose and serum albumin) as well as a commercial collector (Aero 6697). Fourier transform infrared spectroscopy (FTIR) was then used to gain a deeper understanding of the interaction of these compounds with these sulfide minerals. It aims to achieve a better understanding of the surface chemistry of sulfide–water interfaces that improve froth flotation at industrial scale in the step of copper sulfide ore concentration. Zeta potential results show that hydrogen and hydroxide ions are potential determining ions for each sulfide mineral studied. The addition of 50 g/t biosolids or all the other chemicals used in this investigation shift the isoelectric point of chalcopyrite. Under the same conditions, only humic acid significantly affects the zeta potential of molybdenite, making it more negative in the pH range investigated, and shifting its isoelectric point about 6 pH units. These compounds seem to have a poor affinity with pyrite surfaces because their zeta potential is slightly modified. FTIR spectroscopy characterization shows that biosolids and their main components can interact with chalcopyrite, molydenite and pyrite surfaces through a complex mechanism involving chemical or physical linkages. The results reported here seem to indicate that biosolids may be used as new environment-friendly froth flotation agents to concentrate copper and molybdenum sulfide minerals

Sawdust was used as cheap sorbent for the remediation of a diesel and oil fuel-contaminated mining soil whereas biosolids were evaluated as collectors and frothers in froth flotation of copper sulphide ores. The use of these waste materials in copper sulphide ore mining and mineral processing may have a positive impact on the cleaner production of copper from its natural raw sources and may decrease the deleterious effect that mining operations have on the environment. Mixtures of oil fuel contaminated mining soil and sawdust were treated in an aerobic reactor at 50.0% humidity for several days. A significant decrease (over 60.0%) of Volatile Organic Compounds (VOCs) content in these mixtures was obtained after one-month of treatment. Rougher flotation of copper sulphide ores using biosolids - from wastewater treatment plants - and humic acids - a component of biosolids - as collector and frother yielded a copper sulphide concentrate with a copper grade and copper recovery of 0.8% and 26.0%, and 3.5% and 29.7%, respectively. Biosolids and humic acids have shown to be potential candidates to partially substitute traditional organic chemicals used in industrial flotation of copper sulphide ores. This possibility opens up an alternative for greening copper sulphide ore flotation by using more environment friendly flotation reagents. Based on these results, a conceptual model based on industrial ecology and cleaner production principles is proposed for greening the overall copper sulphide ore processing.