This article studies the dynamic multi-hoist scheduling problem in a robotic cell with multi-function and parallel processing units. We consider a general case of a multi-recipe and multi-stage material handling process, which can be encountered in many manufacturing industries such as electronics, chemical, food, automotive, and steelmaking. Scheduling such a process, even for a line with a single hoist, is very complex and has been merely dealt with using exact mathematical methods. We propose a logic-based optimization model to solve the task scheduling and resource allocation problem for a line with multiple hoists. Then necessary and sufficient conditions are presented to avoid collision between the hoists. A compact formulation for these constraints is then integrated into the model. We consider the units with multi-capacity as a special case of parallel units; nevertheless, we drive a more efficient model to deal with this problem in particular. Eventually, the logical constraints are translated into a MILP model that can be solved to optimality to minimize both productivity and hoist movement. In the end, we verify the effectiveness of the proposed method by applying it to various practical problems.

SDGs are playing an increasing role in defining sustainability paths for energy-intensive sectors. In particular, the steel sector is promoting several parallel initiatives as a key player sector in the European process industry. This work describes the major focal trends related to the sustainability of steel and presents the principal EU approaches and initiatives linked with the ESTEP action area. The core sustainability issues related to SDGs in the EU steel sector are presented with a particular focus on the quantification approaches. Then, the paper presents different areas for SDG implementation by single organizations in the EU context. Such areas provide an operational path for managing and implementing SDGs. In particular, the key areas include: (1) roadmapping initiatives with a focus on specific sustainability targets; (2) eco-labelling trends with reference to usage per label typology; (3) reporting initiatives by single organizations with a focus on specific SDGs; and (4) representative EU steel R&D projects related to selected sustainability targets. The discussion part focuses on a critical review of all presented areas to summarise the main paths in adopting SDGs targeted at the EU steel sector level. As the final outcome, prime emerging barriers are suggested as well as critical issues in implementing SDG-based sustainability targets.

In this study, artificial neural networks are adopted to perform multi-step predictions of the power consumed by the refiner of a thermo-mechanical pulping process specialized in medium-density fiberboard production. In this way, the obtained model can be integrated within a model-based control. The refining process is characterized by a large number of variables, and artificial neural networks are a well-established methodology for multivariate data processing, able to identify the non-linear hidden relationship between monitored variables. Both a Long Short-Term Memory network, with stability guarantees, and a Transformer one are implemented due to their ability to model the evolution of dynamical systems. Simulation results prove both models' multi-step prediction capabilities.

A two-layer hierarchical control scheme entirely based on Model Predictive Control (MPC) is proposed for the control of a compressed air network. The high-level exploits the air demand prediction and - through a hybrid MPC - defines the optimal unit commitment and compressor operating points, minimizing the network energy consumption over a long time horizon. At the low-level, compressors are controlled independently to track the references received from the upper layer in the presence of actuation constraints. The proposed control solution can be applied to different network configurations. Simulation results prove the capabilities of the strategy when compared to traditional techniques used nowadays in industry.

The scope of this document is to describe the reference architecture and the collaboration scheme of the high-end optimization tools that are under development in the Tasks 5.2 and 5.3 of the E2COMATION project and that have the purpose of optimizing the operational efficiency of a plant by addressing respectively, the scheduling of the production - considering the energetic and sustainability impacts - and the operation of the on-site multi-energy generating units. The efficient management of the energetic resources in the industrial context requires an energy-oriented multi-layer optimization framework. In particular, the production scheduling and the on-site generation management are mutually dependent and need a proper integration between the dedicated tools. The E2COMATION platform is conceived to provide the factories with sustainability and energy optimization functionalities. To this aim, high-end tools are deployed as a service and operating either in the cloud or on edge, based on the application's specific requirements. A seamless software infrastructure guarantees the integration of the different functionalities in a complete, safe, and efficient environment. A standardized communication protocol permits the management of the data and event streams from the shop-floor level and the integration of the high-end tools.

Conventional Life Cycle Assessment (LCA) that relies on static coefficients is usually based on yearly averages. However, the impacts of electricity supply vary remarkably on an hourly basis. Thus, a company production plan is reassessed to reduce selected LCA impacts due to electricity consumption. To achieve this, the company will need a forecast of hourly LCA impacts due to electricity consumption, which can be directly forecast with the Direct Forecasting (DF) approach. Alternatively, the Electricity Technological Mix Forecasting (ETMF) forecasts the electricity production of the technologies in the mix and subsequently linearly combines it with unitary LCA impact indicators. Here, we assessed different machine learning models to forecast two LCA impact indicators for the consumption of electricity in the Italy-North control zone. The feed-forward neural network (NN) with the ETMF approach was the best perfomer among the assessed forecastingmodels. In our dataset, recurrent neural networks (RNNs) performed worse than feed-forward neural networks. Due to its better forecasting performance, the ETMF approach was preferred over the DF approach. This was due to its flexibility and scalability with easy updates or expansion of the selected forecast indicators, and due to its ability to assess technology-specific errors in the forecasting. Finally, we propose to adopt the correlation of LCA impact indicators within the dataset to select indicators while avoiding unconscious burden-shifting.

This paper analyses the inventory management in the food distribution centres and proposes a solution to cope with the abrupt and planned variations in the upstream demand. Through the mathematical optimisation techniques, total inventory costs are minimised, and customers' service level is maximised. The model is applied to the main distribution centre of Condis supermarkets in Barcelona. The preliminary results show how the model can optimally determine the safety and cycle stock while minimising the overstock situation in different scenarios of demand variabilities. The model is also capable of eliminating the stockout situation and increasing the inventory service level.

The explosive growth of the global market for Carbon-Fiber-Reinforced Polymers (CFRP) and the lack of a closing loop strategy of composite waste have raised environmental concerns. Circular economy studies, including Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), have investigated composite recycling and new bio-based materials to substitute both carbon fibers and matrices. However, few studies have addressed composite repair. Studies focused on bio-based composites coupled with recycling and repairing are also lacking. Within this framework, the paper aims at presenting opportunities and challenges of the new thermosetting composite developed at the laboratory including the criteria of repairing, recycling, and use of bio-based materials in industrial applications through an ex ante LCA coupled with LCC. Implementing the three criteria mentioned above would reduce the environmental impact from 50% to 86% compared to the baseline scenario with the highest benefits obtained by implementing the only repairing. LCC results indicate that manufacturing and repairing parts built from bio-based CFRP is economically sustainable. However, recycling can only be economically sustainable under a specific condition. Managerial strategies are proposed to mitigate the uncertainties of the recycling business. The findings of this study can provide valuable guidance on supporting decisions for companies making strategic plans.

Life Cycle Assessment (LCA) computes potential environmental impacts of a product or process. However, LCAs in the industrial sector are generally delivered through static yearly analyses which cannot capture any temporal dynamics of inventory data. Moreover, LCA must deal with differences across background models, Life Cycle Impact Assessment (LCIA) methods and specific rules of environmental labels, together with their developments over time and the difficulty of the non-expert organization staff to effectively interpret LCA results. A case study which discusses how to manage these barriers and their relevance is currently lacking. Here, we fill this gap by proposing a general methodology to develop a modular tool which integrates spreadsheets, LCA software, coding and visualization modules that can be independently modified while leaving the architecture unchanged. We test the tool within the ORI Martin secondary steelmaking plant, finding that it can manage (i) a high amount of primary foreground data to build a dynamic LCA; (ii) different background models, LCIA methods and environmental labels rules; (iii) interactive visualizations. Then, we outline the relevance of these capabilities since (i) temporal dynamics of foreground inventory data affect monthly LCA results, which may vary by ±14% around the yearly value; (ii) background datasets, LCIA methods and environmental label rules may alter LCA results by 20%; (iii) more than 105 LCA values can be clearly visualized through dynamically updated dashboards. Our work paves the way towards near-real-time LCA monitoring of single product batches, while contextualizing the company sustainability targets within global environmental trends.

The raising environmental concerns related to fossil fuel-based plastic packaging resulted in policy efforts for reducing their use and introducing bio-based materials. However, the availability of bio-based material suppliers may be currently limited. At the industrial level, Life Cycle Assessment (LCA) can provide decision support to companies willing to substitute fossil fuel-based with bio-based plastic packaging. In this context, a detailed modelling of the plastic supply chain can markedly alter the LCA results and the consequent interpretation. The present work is based on preliminary results from the Italian project "Plastic New Deal", which aims to support small enterprises on substituting fossil fuel-based plastic with other materials. The goal of this study is to investigate the variability of LCA results related to the substitution of fossil fuel-based low-density polyethylene (LDPE) with bio-based LDPE (bio-LDPE) and polylactic acid (PLA), used as film for packaging. A total of 11 scenarios was built, modelling the country of plastic manufacturing and type of feedstock. LCA results of bio-based plastics by country vary up to +165\% and -95\% with respect to the average, which is much higher than the one of fossil fuel-based LDPE across all the impact categories considered in the study. This is due to the additional variability in the cultivation and conversion processes, originated from different types of feedstocks. Uncertainties on End-of-Life (EoL) treatment processes of plastic packaging and on accounting for life cycle biogenic carbon dioxide exchanges might further alter the LCA results for the climate change total category. The present paper attempts to highlight potential issues associated to a generic LCA which does not sufficiently account for the company context, empowering the accuracy of LCA as decision support tool and opening a discussion on how to improve the reliability of practical LCA recommendations.

A hierarchical architecture for the optimal management of an ensemble of steam generators is presented. The subsystems are coordinated by a multilayer scheme for jointly sustaining a common load. The high level optimizes the load allocation and the generator schedule, considering activation dynamics by a hybrid model. At the medium level, a robust tube-based model predictive control (MPC) tracks a time-varying demand using a centralized--but aggregate--model, whose order does not scale with the number of subsystems. A nonlinear optimization, at medium level, addresses MPC infeasibility due to abrupt changes of ensemble configuration. Low-level decentralized controllers stabilize the generators. This control scheme enables the dynamical modification of the ensemble configuration and plug and play operations. Simulations demonstrate the approach potentialities.

The current traditional mechanical recycling of Polyethylene Terephthalate (PET) grinds the waste into granulate, yet the resulting secondary material quality is strongly dependent on the efficiency of the selection processes, leading to the requirement of an integration of fossil PET to assure the bottle-grade quality is reached. Instead, the novel chemical recycling gr3n technology depolymerizes the waste PET back into the constituent monomers with a resulting quality that is comparable to the virgin product, due to a more efficient separation of impurities. In order to estimate the environmental impacts related to the introduction of this technology in the related market, Consequential Life Cycle Assessment (CLCA) is particularly indicated. Among the consequential approaches, we adopt the Stochastic Technology Choice Model, as it is able to model the technological mixes typical of markets based on costs and production capacities, while its stochasticity suits the need to manage the uncertainty of future market conditions. Indeed, the assessment of the expected technological mixes contributing to the same function and the quality of the recycled material are key to evaluate the variation in marginal LCA impacts due to the introduction of the gr3n technology. We assess the marginal LCA impacts of the European bottle-grade PET market in two scenarios: one in which the gr3n technology is not available and one in which this technology is present. To correctly evaluate the difference between these two scenarios, we perform a paired simulation. Here we show that the populations related to this difference show more than 50% negative results in 12 out of 16 impact indicators and more than 75% of negative results in 9 out of 16 impact indicators. In particular, a median 0.13 kg CO2-eq per kg bottle-grade PET could be saved by the introduction of gr3n, equivalent to a 5% reduction. We show that the 5-95 percentiles range of the difference between the two scenarios is only 17.7% of the average range defined by the two separate scenarios distributions, confirming previous findings from the literature. The robustness of the results is tested through three sensitivity analyses. Therefore, policy makers should focus on limiting the increase in marginal demand of PET and on creating fair conditions for this chemical recycling technology to be deployed to complement mechanical recycling in reducing virgin PET production, thus decreasing potential environmental impacts and fostering a more circular economy. The positive performance of the novel technology is strongly related to the increased substitution of waste treatment processes, such as incineration and landfill, and to the increased quality of the recycled product: this environmental profile could further improve as the novel technology will scale up industrially.

Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical approach is proposed to control coupled energy nonlinear systems. The high level hybrid optimization defines the unit commitment, with the optimal transition strategy, and best production profiles. The low level dynamic model predictive control (MPC), receiving the set-points from the upper layer, safely governs the systems consid- ering process constraints. To enhance the overall efficiency of the system, a method to optimal start-up the GU is here presented: a linear parameter varying MPC computes the optimal trajectory in closed-loop by iteratively linearising the system along the previ- ous optimal solution. The introduction of an intermediate equilibrium state as additional decision variable permits the reduction of the optimization horizon,while a terminal cost term steers the system to the target set-point. Simulation results show the effectiveness of the proposed approach.

This work presents a hierarchical architecture for the optimal management of an ensemble of steam generators, which needs to sustain jointly a common load. The coordination of independent subsystems is provided by a multi-layer control scheme. A high-level optimizer computes the optimal shares of production to be allocated to single generators. At medium level, a robust tube-based Model Predictive Control (MPC) is proposed to track the time-varying demand of the ensemble using a centralized, but aggregate model, whose order does not scale with the number of subsystems. At low level, decentralized controllers are in place to stabilize the internal boiler pressures. The control architecture enables dynamically the modification of the ensemble configuration and plug and play operations. Simulation results are reported to demonstrate the potentialities of the proposed approach.

Modern automation systems are asked to provide a step change toward flexibility and reconfigurability to cope with increasing demand for fast changing and highly fragmented production--which is more and more characterising the manufacturing sector. This reflects in the transition from traditional hierarchical and centralised control architecture to adaptive distributed control systems, being the latter capable of exploiting also knowledge-based strategies toward collaborating behaviours. The chapter intends to investigate such topics, by outlining major challenges and proposing a possible approach toward their solution, founded on autonomous, self-declaring, knowledge-based and heterarchically collaborating control modules. The benefits of the proposed approach are discussed and demonstrated in the field of re-manufacturing of electronic components, with specific reference to a pilot plant for the integrated End-Of-Life management of mechatronic products.

In this paper we propose a Nonlinear Model Predictive Control (NMPC) scheme for the optimization of the start-up procedure of a nonlinear boiler model. The proposed formulation of the MPC problem allows for a significant reduction of the optimization horizon with respect to state of the art - often open loop - optimization approaches (that commonly solve the nonlinear program for a long horizon that includes the whole start-up time), while guaranteeing the recursive feasibility and remarkable performances. A numerically efficient implementation of NMPC is obtained by subsequent linearisation of the system along the predicted trajectory. Simulation results show the advantages of the proposed method with respect to standard manual procedures and to open-loop optimization approaches.

The application of LCA in the energy consumption management can address the sustainability of energy systems. The chapter first aims at summarizing general trends in addressing environmental implication of energy use. Second, LCA methodology is briefly introduced in order to clarify its potentialities and general use in the energy field area. In particular, LCA can contribute to select the best technological choices for an energy system. A challenge in the use of LCA is identified in the representation of a complex system in which the energy producers' contribution changes on a temporal basis. Two approaches are proposed for the LCA use in the short-term perspective: attributional LCA and consequential LCA. The proposed approach examines the application of LCA in a short-term perspective. Both approaches can be used to analyze an efficient configuration of the system. However, the more the temporal and geographical area is restricted, the more specific issues have to be adopted to provide a reliable analysis. In particular, consequential and attributional approaches should be used under different hypotheses and with proper adaptation. The proposed approach examines the application of consequential LCA in a short-term perspective, defined as the time span in which the market system has not reacted to a change yet. Moreover, it could claim environmental impact savings in the presence of an accurate model that is able to predict the hourly marginal technology of the near future (one day to 1 week). The future application of the proposed approach would be a tool that manages to assess the best hourly consumption trajectory in order to minimize environmental impacts.

The result addresses the control software devoted to piloting Additive manufacturing machine with Multi-material single screw extruder for the realisation of personalised Movement Assistive Devices (MADs) The control software is a PC-based application suite and it consists of several software modules cooperating for the control of the whole manufacturing process: oA Rhino-script processing data from Rhinoceros CAD to deliver dedicated MAD CAM has been realized. oAn GUI with HMI functions for governing the various steps for logical sequencing (file import and machine configuration), processing (printing session configuration) and session launch oA logic control module, in charge of performing the handling of machine logic control (auxiliaries system, safety, thermal management) oA Numeric Control kernel of algorithms in charge of the interpolation of six-axes (linear X-Y-Z axes + A-B-C rotary axes) together with the extruder (interpolated as 7th axis) has been totally developed in C/C++ using open LINUX RTAI OS, IGH EtherCAT master.

The developed prototype is an advanced 6 axis degree of freedom deposition machine, guaranteeing the capability to material free-form deposition and therefore allowing complex structures creation. The prototype is also equipped with an innovative single screw material extruder which allows to change the composition of the material to be extruded according to requirements. The machine is thermally insulated and has a controlled temperature to avoid the rapid passage of temperature from the extruder to the external environment. The work area is a cylinder of 300mm in diameter and a height of 500mm. This allows the realization of a MAD in one time. Also the support surface on which the MAD is printed is controlled in temperature to help the adhesion between the extruded material and the plate itself.

Present document addresses solutions for assembly, fast detaching and replacement of MAD parts and components, elaborated and engineered with respect to outcomes of activities of tasks 5.2 and 5.3. Main aspects covered by the activities of the task reported in this document deal with: ? Section 3 - MovAiD product: main parts and features to be assembled are discussed ? Section 4 - Assembly mechanisms among structural part and body fitting parts on upper and lower limb MADs are described considering: o Approaches to mechanical assembly of structural and body fitting parts and related final adopted solution o Connections of conductive tracks ? Section 5 - Housing solutions for battery fast replacement are discussed in terms of securing enclosure assemblies and main body of the housing ? Section 6 - Housing solutions for sensor access maintenance are addressed ? Section 7 - Conclusions Technical solutions are presented and discussed also with reference to their impact on the value chain in terms of component costs and procurement aspects.