Search

The rapidly increasing amount of information and entries in abstract and citation databases steadily complicates the information retrieval task. In this study, a novel query-by-document approach using Monte-Carlo sampling of relevant keywords is presented. From a set of input documents (seed) keywords are extracted using TF-IDF and subsequently sampled to repeatedly construct queries to the database. The occurrence of returned documents is counted and serves as a proxy relevance metric. Two case studies based on the Scopus® database are used to demonstrate the method and its key advantages. No expert knowledge and human intervention is needed to construct the final search strings which reduces the human bias. The methods practicality is supported by the high re-retrieval of seed documents of 7/8 and 26/31 in high ranks in the two presented case studies.

To address the urgent need to transform the current economic paradigm towards a more circular one, this contribution proposes a framework for assisting the selection of the most promising waste-to-resource alternatives. An ontology-based approach is used to manage and centralize the knowledge across the different stages of this methodology. Feasible combinations of operations lead to processing routes connecting waste with products and the paths are assessed to pre-select the most suitable ones. Then, the network is optimized, and a set of configurations are obtained in order to maximize economic profit and minimize environmental impact. The performance of the proposed framework is illustrated through a case study for the treatment of mixed plastic waste. Results show how pyrolysis-based treatments lead to suitable options according to some specified objectives but also reveal the main drawbacks and conditions that these treatments should improve. The work also demonstrates how to efficiently assess a large number of options, and discusses how to build and improve new processing networks.

Chemical process operation optimization aims at obtaining the optimal operating set-points by real-time solution of an optimization problem that embeds a steady-state model of the process. This task is challenged by unavoidable Uncertain Parameters (UPs) variations. MultiParametric Programming (MPP) is an approach for solving this challenge, where the optimal set-points must be updated online, reacting to sudden changes in the UPs. MPP provides algebraic functions describing the optimal solution as a function of the UPs, which allows alleviating large computational cost required for solving the optimization problem each time the UPs values vary. However, MPP applicability requires a well-constructed mathematical model of the process, which is not suited for process operation optimization, where complex, highly nonlinear and/or black-box models are usually used. To tackle this issue, this paper proposes a machine learning-based methodology for multiparametric solution of continuous optimization problems. The methodology relies on the offline development of data-driven models that accurately approximate the multiparametric behavior of the optimal solution over the UPs space. The models are developed using data generated by running the optimization using the original complex process model under different UPs values. The models are, then, used online to, quickly, predict the optimal solutions in response to UPs variation. The methodology is applied to benchmark examples and two case studies of process operation optimization. The results demonstrate the methodology effectiveness in terms of high prediction accuracy (less than 1% of NRMSE, in most cases), robustness to deal with problems of different natures (linear, bilinear, quadratic, nonlinear and/or black boxes) and significant reduction in the complexity of the solution procedure compared to traditional approaches (a minimum of 67% reduction in the optimization time).

The global economy is shifting towards more sustainable sources of energy. The transportation sector is a remarkable example of this fact, where biofuels have emerged as promising alternatives to traditional fossil fuels. This work presents a techno-economic and environmental assessment of existing liquid fuels in hard-to-decarbonize sectors and their emerging renewable substitutes. The comparison focuses on fossil-based, biomass-derived and plastic waste-sourced fuel alternatives that can be used in spark-ignition (gasoline) engines and compression-ignition (diesel) engines. Results for diesel substitutes prove a superior performance of plastic waste pyrolysis oil in terms of production cost reduction (-25% compared to diesel) and “well-to-tank” life cycle impact reduction (-54% human health, -40% ecosystems, -98% resources). Consequently, research and development towards conversion of plastic waste into fuels should be extended with the goal of making the technology more accessible and robust in terms of fuel quality. On the contrary, the results for the gasoline alternatives are not as conclusive: Bioethanol and ethanol from plastic pyrolysis have considerably lower impact on resource scarcity than gasoline (-80% and -35% respectively) and higher on the other two life cycle endpoint categories, but they have higher production costs when compared to gasoline (+57% and +130% respectively). While blends of gasoline with pyrolysis-sourced ethanol can reduce impacts on human health and ecosystems, blends with bioethanol entail lower impacts on resource scarcity and increase economic profitability. This allows fuel providers to offer trade-off solutions in form of blends based on their priorities.

A soft-sensing methodology applicable to batch processes operated under changeable initial conditions is presented. These cases appear when the raw materials specifications differ from batch to batch, different production scenarios should be managed, etc. The proposal exploits the capabilities of the machine learning techniques to provide practical soft-sensing approach with minimum tuning effort in spite of the fact that the inherent dynamic behavior of batch systems are tracked through other online indirect measurements. Current data modeling techniques have been also tested within the proposed methodology to demonstrate its advantages. Two simulation case-studies and a pilot-plant case-study involving a complex batch process for wastewater treatment are used to illustrate the problem, to assess the modeling approach and to compare the modeling techniques. The results reflect a promising accuracy even when the training information is scarce, allowing significant reductions in the cost associated to batch processes monitoring and control.

Additive manufacturing (AM) or 3D printing offers a new paradigm for designing and developing chemical reactors, in particular, prototypes. The use of 3D printers has been increasing, their performance has been improving, and their price has been reducing. While the general trend is clear, particular applications need to be assessed for their practicality. This study develops and follows a systematic approach to the prototyping of Advanced Oxidation Processes (AOP) reactors. Specifically, this work evaluates and discusses different printable materials in terms of mechanical and chemical resistance to photo-Fenton reactants. Metallic and ceramic materials are shown to be impracticable due to their high printing cost. Polymeric and composite materials are sieved according to criteria such as biodegradability, chemical, thermal, and mechanical resistance. Finally, 3D-printed prototypes are produced and tested in terms of leakage and resistance to the photo-Fenton reacting environment. Polylactic acid (PLA) and wood–PLA composite (Timberfill®) were selected, and lab-scale raceway pond reactors (RPR) were printed accordingly. They were next exposed to H2O2/Fe(II) solutions at pH = 3 ± 0.2 and UV radiation. After 48 h reaction tests, results revealed that the Timberfill® reactor produced higher Total Organic Carbon (TOC) concentrations (9.6 mg·L−1) than that obtained for the PLA reactor (5.5 mg·L−1) and Pyrex® reactor (5.2 mg·L−1), which suggests the interference of Timberfill® with the reaction. The work also considers and discusses further chemical and mechanical criteria that also favor PLA for 3D-printing Fenton and photo-Fenton reactors. Finally, the work also provides a detailed explanation of the printing parameters used and guidelines for preparing prototypes.

Process modeling requires both data (chemical reaction yields, kinetic constants, cost estimates, environmental indicators, etc.) and knowledge (operation models and formulations, alternative processes and technologies, etc.). Searching in databases and published research may provide such information, but there is a lack of systematic methods and tools guiding this procedure. The present work describes and assesses an information retrieval methodology that is part of a proposed retrieval and extraction cycle addressing this problem. Two query construction methods for sampling academic databases are proposed assessed and compared. Departing from a seed corpus of a limited number of papers, Scopus® is used as an academic database to retrieve literature containing information associated with pyrolysis processes of waste plastic. It is found that, with minimal human intervention, the methodology is able to return a ranked list of candidate documents that have a considerable (linguistic) relevance.

This work studies in detail the photo-Fenton degradation process of Paracetamol (PCT) on an annular pilot-plant reactor using Computational Fluid Dynamics (CFD). A cylindrical lamp emission model was originally implemented over the structure of the OpenFOAM(R) platform and a multicomponent reaction mixture model was used to compute the temporal evolution of the different species at each point of the reactor. Once the proposed model was experimentally validated, the influence of different operating conditions (i.e. different strategies for hydrogen peroxide (H 2 O 2 ) dosage, use of low recirculation flow rates (Qr), and a completely uncovered lamp setup) was studied. The results of the analysis showed that a double addition of H 2 O 2 (50% before the tank and 50% before the reactor) significantly reduces the reaction times of the process. Moreover, the overall PCT degradation rate does not change when Qr is increased, thus allowing the system to be operated with a recirculation flow three times lower than that the one used in the experiments. Thereby, the developed model allows identifying the reaction conditions that maximize the overall PCT conversion, making efficient use of H 2 O 2 (main chemical reagent) and reducing the electrical energy consumption (recirculation flow) by operating the system under conditions present in large-scale photochemical reactors.

Water treatment is traditionally seen as an "end-of-pipe" solution to deal with contaminated water satisfying discharge regulations at a minimum expense. However, the reuse of treated water as regenerated water is a promising strategy to counteract water scarcity. This approach to transform waste into resources is motivated by the circular economy paradigm. This study presents a mathematical programming approach to target both the environmental and economic benefits of water systems by introducing additional regeneration units to close the loop. In addition to water users and authorities, the approach also considers operators and dealers, which are revealed as key stakeholders. Hence, the feasible region of the regeneration units design specifications is determined and visualized through a multi-objective optimization approach targeting the systems operating cost and freshwater consumption. Its application is demonstrated on a benchmark case study from the literature, revealing a potential economic benefit of 37.5% and a freshwater reduction of 80.9% over the case without regeneration units. Furthermore, we show that a cooperative exchange strategy leads to higher benefits compared to the solutions presented in the literature. Finally, we demonstrate how the barrier plots introduced in this work can be used by different stakeholders in the water market to support their decision-making.

This work addresses the challenge of designing H2O2 dosage strategies for improving photo-Fenton applications, as well as for further understanding the effect of the dosage. The developed strategy focuses on the limitations of the current solution schemes by adopting a hybrid methodology between open and closed loop approaches and it is based on three different stages: (i) one-shot initial H2O2 addition (ii) continuous H2O2 dosage until reaching a specific dissolved oxygen (DO) level and (iii) on-off control of H2O2 dosage using DO slope as control variable. The proposed strategy is validated in the remediation of a Paracetamol solution (100 mg L−1) and the results are assessed using H2O2 consumption and mineralization rate and level as performance criteria. The final tuning of the proposed strategy consists of: (i) only 40% of the stoichiometric H2O2 concentration, (ii) continuous feeding of H2O2 until a 4 mg L−1 DO concentration is attained, and (iii) on-off control dosage selecting DO slope set-points in 0.1 and 0.2 mg L−1 min−1. These scheme and settings show an improvement of the process performance by ~ 15% with respect to the same H2O2 amount in a single-addition. This work contributes to improving photo-Fenton operations and designing more efficient processes.

This work addresses the dosage of H2O2 in photo-Fenton processes and the monitoring of Dissolved oxygen (DO) that can be used to drive the dosage of H2O2. The objective of this work is to show that a smarter monitoring of a process variable such as DO (for which on-line measurement can be inexpensively obtained) enables the proposal and implementation of efficient dosage strategies. The work explores the application of a recent proposed strategy consisting of: (i) initial H2O2 addition, (ii) continuous H2O2 addition until a DO set up is reached, and (iii) automatic H2O2 addition by an on-off control system based on DO slope monitoring, and applies it to the treatment of different individual contaminants and their mixtures (paracetamol and sulfamethazine). The assays performed following this dosage strategy showed improved values of TOC removed per H2O2 consumed. For the case of sulfamethazine, this improvement increased up to 25–35% with respect to the efficiency obtained without dosage. Furthermore, a deeper analysis of the results allowed detecting and assessing the opportunity to redesign the dosage scheme and reduce its complexity and the number of control parameters. The promising results obtained are discussed in regard of future research into further increasing the simplicity and robustness of this generalized control strategy that improves the applicability of the photo-Fenton process by reducing its operating costs and increasing automation.

Utility exchanges between different plants have shown to produce large energy savings, extending the potential advantages of Energy/Process Integration through Industrial Symbiosis principles. Systematic approaches to determine such exchanges in industrial networks have been already proposed, although some of them are only applicable to specific situations and some others introduce the figure of a central authority. However, assuming such a figure in non-cooperative situations may restrict the economic benefit of some companies involved, thus discouraging their participation and preventing eventual agreements. The aim of this work is to develop an optimization model that allows analyzing the different symbiosis alternatives in different conflicting situations, even without the presence of any authority. Scenarios inspired by Game Theory have been considered. The problem has been modelled using a Mixed Integer Linear Programming (MILP) formulation and its capacities are illustrated through a particular case from the literature. Results show that the method allows establishing utility exchanges between different plants, which can improve the energetic, economic and environmental efficiency of all of them, as well as the whole set. Considering cooperative scenarios may allow determining solutions producing total energy savings and cost reductions, but without taking the specific interests of individual companies into account. On the other hand, considering non-cooperative scenarios ensures desirable outcomes from the eventual agreements for each company. Furthermore, the model is able to identify the economic barriers of the companies for participating, thus, being a useful and applicable tool that may improve decision-making support for managing utility networks in such situations.

The determination of the hydrogen peroxide dosage scheme that minimizes hydrogen peroxide consumption while meeting the specified treatment outcome is crucial for Fenton and photo-Fenton processes. The challenge is building a methodology that provides the optimal dosage profile. However, the lack of detailed dynamic models prevents exploiting model-based optimization methods that have proved successful in other applications. Thus, this work addresses this challenge by providing a problem formulation identifying and discussing objectives and constraints, and the nature of the optimal solution. From this point, the work presents a novel dosage model and a consequent methodology aimed at experimentally optimizing the dosage profile along a discretized time horizon following recipe optimization concepts. The approach is parallel to the numerical solution of the model-based optimization problem posed by hydrogen peroxide dosage. The proposed methodology is validated in the remediation of a Paracetamol (PCT) solution, and the obtained results are assessed and discussed in regard of the evolution of the concentration of hydrogen peroxide, the contaminant (PCT), and Total Organic Carbon (TOC). The concentration of dissolved oxygen (DO), which is also monitored, allows providing a more comprehensive explanation of the nature of the process.

Monte Carlo methods are defined broadly as a statistical approach to provide approximate solutions to mathematically complex optimization or simulation problems by using random sequences of numbers. The two main advantages of Monte Carlo methods are perhaps that the concept is relatively simple and easy to use, and the same method has a sound basis. The Law of large numbers ensures that the Monte Carlo solution converges asymptotically to the true solution of a problem and since its first formulation by Metropolis and Ulam (1949), Monte Carlo methods have seen widespread application across many scientific and engineering disciplines.

The concept of circular economy has gained wide interest in industry as a powerful strategy to reduce impacts while remaining economically competitive. Here, we explore the benefits of this approach in the chemical industry, quantifying explicitly the economic and environmental benefits of ethylene recovery from polyethylene plastics using process modeling coupled with Life Cycle Assessment (LCA). Our results show that the recovered ethylene represents a win-win alternative, offering a highly competitive cost of 0.386 €/kg vs 0.835 €/ kg with the standard naphtha-based route. Furthermore, substantial reductions of as much as 87%, 89%, and 164% could also be attained (compared to the naphtha-based ethylene) in impacts on human health, ecosystem quality, and resources scarcity, respectively. Overall, this work aims to promote the adoption of circular economy strategies in the chemical sector, while highlighting the role of process modeling, LCA, and systems thinking in quantifying the associated benefits to build strong cases for policymaking.

Circular economy appears to be the only suitable approach for closing the materials loop and so to make sustainable current development levels. In this work, a procedure to systematically generate a list of potential waste-to-resource alternatives, using a semi-automatic ontological framework, is presented. The generated alternatives are then analysed and sorted according to different indicators of their economic performance, environmental impact and technological maturity. The approach is illustrated through its application to the management of a residue composed by a mixture of plastic materials.

Wastewater treatment plants (WWTPs) are an example of available but underutilized waste-to-resource processes. Economic benefit opportunities can be generated through the design and operation of WWTPs following a circular economy policy. At the same time, the environmental performance is also enhanced, since water scarcity is counteracted by replacing freshwater usage with reclaimed water. In this work, we develop a MILP mathematical model representing the main characteristics of multi-contaminant wastewater exchange networks and validate it through a case study that is inspired of a WWTP located in Barcelona, Spain. As a result, we find an optimal network that can reduce the water treatment cost from 0.404 €/m³ to 0.312 €/m³. Moreover, the model allows considering the possibility of retrofitting the existing WWTP by constructing an additional treatment line and drawing the barriers for this line to achieve the targeted wastewater treatment cost. These results demonstrate that changing the role of WWTPs from an “end-of-the-pipe” solution towards a waste-to-resource route bears an unexplored potential that should be considered by all actors in the water market.

This work proposes a methodology for multivariate dynamic modeling and multistep-ahead prediction of nonlinear systems using surrogate models for the application to nonlinear chemical processes. The methodology provides a systematic and robust procedure for the development of data-driven dynamic models capable of predicting the process outputs over long time horizons. It is based on using surrogate models to construct several nonlinear autoregressive exogenous models (NARX) with each one approximating the future behavior of one process output as a function of the current and previous process inputs and outputs. The developed dynamic models are employed in a recursive schema to predict the process future outputs over several time steps (multistep-ahead prediction). The methodology is able to manage two different scenarios: (1) one in which a set of input–output signals collected from the process is only available for training and (2) another in which a mathematical model of the process is available and can be used to generate specific datasets for training. With respect to the latter, the proposed methodology includes a specific procedure for the selection of training data in dynamic modeling based on design of computer experiment (DOCE) techniques. The proposed methodology is applied to case studies from the process industry presented in the literature. The results show very high prediction accuracies over long time horizons. Also, owing to the flexibility, robustness, and computational efficiency of surrogate modeling, the methodology allows dealing with a wide range of situations, which would be difficult to address using first-principles models.

In this work, the effects of multi renewable resource-based energy integration in material supply chains are analysed under stochastic and deterministic conditions. The proposed approach is based on the development of a Multi-objective model to optimize the design and planning decisions of country-size SCs in the existence of conflicting objectives. A multi-scenario mixed-integer linear model is proposed and the capabilities of the approach are examined through a case study from a Sugar cane industry. The integrated energy/material supply chains model shows more economical feasibility than standalone ones and the stochastic solutions in this work demonstrate more robustness in the economic performance of the SC comparing to the deterministic one at any level of the environmental impact. In fact, the stochastic approach optimizes the conflicting objectives as well as improves the whole system robustness compared to the deterministic one and should be therefore the preferred choice in practice.

Fenton and photo-Fenton are Advanced Oxidation processes driven by the supply of hydrogen peroxide (H2O2). However, excessive hydrogen peroxide supply results in unproductive reactions and causes the process performance to decay. Thus, the goal of this work is to systematically compare different H2O2 dosage strategies using a new approach based on monitoring dissolved oxygen as a performance indicator, which is assumed to directly reveal the inefficient consumption of H2O2. The systematic approach proposed is a preliminary test to stablish a general methodology to define an optimal dosage scheme.

The benefits of the circular economy paradigm have been proven during the past two decades, but its application poses some challenges that still need to be tackled. This contribution presents a systematic way to generate a list of potential waste-to-resource technologies based on the use of a semi-automatic ontological frameworks. The ontology is instantiated, giving the possibility to generate and assess a list of transformation processes alternatives, according to the potentially available waste streams and resource requirements in a specific area and/or sector. The resulting list is then analyzed and classified according to pre-established parameters, thus presenting which are the potentially best alternatives to close the material loops and recover chemical resources from available waste. The capabilities of the method to identify promising transformation technologies are assessed through an illustrative case study: the evaluation of different routes for the treatment of plastic waste materials, with the focus on chemical recycling.

A kinetic model describing Fenton and photo-Fenton degradation of paracetamol (PCT) and consumption of hydrogen peroxide (H 2 O 2 ) was proposed. A set of Fenton and photo-Fenton experiments (18 runs in total) was performed by fixing the initial concentration of PCT to 40 mg L −1 and varying the initial concentrations of H 2 O 2 and ferrous ion, Fe 2+ . The experimental set-up was a well-stirred annular photoreactor equipped with an actinic BL TL-DK 36 W/10 1SL lamp. Experimental results highlighted that PCT is no more detected by HPLC analysis within a minimum reaction time of 2.5 and a maximum reaction time of 15.0 min. Besides, a maximum conversion of total organic carbon (TOC) of 68.5% was observed after 75 min of reaction in case of using UV radiation and the highest concentrations of the Fenton reagents. The experimental data were used to fit the kinetic model. The radiation field inside the reactor was taken into account through the local volumetric rate of photon absorption, evaluated by assuming a line source model with spherical and isotropic emission. The kinetic parameters were estimated by using a non-linear least-squares regression procedure and root mean square errors (RMSE) were calculated in order to validate the feasibility of the proposed model. A good agreement between experimental and predicted data was observed and the lowest values of RMSE resulted in 5.84 and 9.59% for PCT and H 2 O 2 normalized concentrations, respectively. Fil: Audino, Francesca. Universitat Politècnica de Catalunya; España Fil: Conte, Leandro Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Schenone, Agustina Violeta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Pérez-Moya, Montserrat. Universitat Politècnica de Catalunya; España Fil: Graells, Moisès. Universitat Politècnica de Catalunya; España Fil: Alfano, Orlando Mario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina

This work aims at proposing and validating a model that can be exploited for the future development of industrial applications (e.g., process design and control) of Fenton and photo-Fenton processes. Hence, a compromise modeling solution has been developed between the non-generalizable accuracy of the first principles models (FPMs) and the oversimplification of the empirical models (EMs). The work presents a novel model of moderate complexity that is simplified enough to be generalizable and computationally affordable, while retaining physical meaning. The methodology is based on a general degradation mechanism that can be algorithmically generated from the carbon number of the target compound, as well as from the knowledge of two kinetic parameters, one for the faster initial rate and the other one for the subsequent degradation steps. The contaminant degradation mechanism has been combined with an appropriately simplified implementation of the well-known Fenton and photo-Fenton kinetics. This model describes the degradation not only of the target compound and of the oxidant, but also of total organic carbon (TOC), which is used to define the overall quality of the water. Experimental design techniques were used along with a non-conventional modeling methodology of programmable process structures (PPS). This novel modeling approach was applied and validated on the degradation of three model compounds. A successful prediction of the evolution of the contaminants H2O2 and TOC was confirmed and assessed by the root mean square error (RMSE).

The benefits of the circular economy have been proven during the past two decades, but its application poses some challenges. In particular, the increasing number of potential waste-to-resource processing alternatives obstructs the identification of the most promising ones, besides the lack of efficient knowledge management tools and standardized assessment procedures. This contribution presents a systematic way to generate and assess new processing paths including waste-to-resource technologies, based on the use of a semi-automatic ontological framework. An ontology is filled with transformation processes; then, a number of alternative paths are generated and assessed, according to a potentially available waste. The resulting list is classified according to pre-established parameters, thus presenting which are the potentially best alternatives to close the material loops and recover chemical resources. The proposed method is tested through the generation and evaluation of different routes for the treatment of plastic waste materials, with a special focus on chemical recycling.

Carbon capture and storage (CCS) and carbon capture and utilisation (CCU) are acknowledged as important R&D priorities to achieve environmental goals set for next decades. This work studies biomass-based energy supply chains with CO2 capture and utilisation. The problem is formulated as a mixed-integer linear program. This study presents a flexible supply chain superstructure to answer issues on economic and environmental benefits achievable by integrating biomass-coal plants, CO2 capture and utilisation plants; i.e. location of intermediate steps, fraction of CO2 emissions captured per plant, CO2 utilisation plants' size, among others. Moreover, eventual incentives and environmental revenues will be discussed to make an economically feasible project. A large-size case study located in Spain will be presented to highlight the proposed approach. Two key scenarios are envisaged: (i) Biomass, capture or utilisation of CO2 are not contemplated; (ii) Biomass, capture and CO2 utilisation are all considered. Finally, concluding remarks are drawn.

Fenton, photo-Fenton, and photo-induced oxidation, were investigated and compared for the treatment of 0.26 mmol L&minus, 1 of paracetamol (PCT) in a deionised water matrix, during a reaction span of 120.0 min. Low and high Fenton reagent loads were studied. Particularly, the initial concentration of Fe2+ was varied between 0.09 and 0.18 mmol L&minus, 1 while the initial concentration of H2O2 was varied between 2.78 and 11.12 mmol L&minus, 1. The quantitative performance of these treatments was evaluated by: (i) measuring PCT concentration, (ii) measuring and modelling TOC conversion, as a means characterizing sample mineralization, and (iii) measuring cytotoxicity to assess the safe application of each treatment. In all cases, organic matter mineralization was always partial, but PCT concentration fell below the detection limit within 2.5 and 20.0 min. The adopted semi-empirical model revealed that photo induced oxidation is the only treatment attaining total organic matter mineralization ( &xi, MAX = 100% in 200.0 min) at the expense of the lowest kinetic constant (k = 0.007 min&minus, 1). Conversely, photo-Fenton treatment using high Fenton reagent loads gave a compromise solution ( &xi, MAX = 73% and k = 0.032 min&minus, 1). Finally, cytotoxicity assays proved the safe application of photo-induced oxidation and of photo-Fenton treatments using high concentrations of Fenton reagents.

Industrial symbiosis plays an important role in the process industry, where an effective management of resources can bring both economic and environmental benefits. Traditionally, the design and extension of eco-industrial parks has been mainly based on intuition and experience; yet, the number of actors involved, the number of flows to manage and the different nature of the materials and energy exchanged lead to complex problems that can benefit from the use of systematic decision-making methods. The aim of this contribution is to produce useful tools to identify the most favourable synergies and transformations for a process network. The proposed model has been tested on a case study that consists of an ethylene and chlorine based eco-industrial park. Results confirm the capabilities of the proposed targeting methodology to match sources and sinks of resources.

Increasing water demand by the process and allied industries coupled with global water stress and scarcity have underlined the importance of water as a crucial resource and increased the need for widespread adoption of water reuse and recycle. Early research based on water-pinch analysis addressed the use of systematic methods to identify the most promising opportunities for water reuse within a process plant. More recent work has addressed similar concepts in the framework of industrial symbiosis and Eco-Industrial Parks (EIP) under the assumption of the leadership of an EIP authority. However, many wastewater plants continue processing wastewater to condition water for disposal, which means meeting the limits given by regulations at a minimum cost. These wastewater treatment plants may play a role in a growing market of regenerated water in which an increasing number of businesses and public services would be demanding water with different quality specifications. Hence, this work is presenting an MINLP model aimed at exploiting the flexibility of a wastewater treatment plant and maximizing its profit within this market. Results and discussion are provided in regard of a case study based on a wastewater treatment plant nearby Barcelona.

Recently, process industries have experienced a significant pressure to shift from centralized energy supplying systems to the in-situ exploitation of renewable resources. Special attention has been paid to multi resource-based energy systems, a particular case of distributed generation where processing nodes include energy generation and can operate either grid-connected or isolated. This work proposes a general model to determine the optimal retrofitting of a supply chain integrating renewable energy sources under uncertain conditions and to analyze the effect of different planning horizons in the solution. The proposed mixed integer linear programming (MILP) formulation allows determining the best combination of available technologies that satisfies the internal energy demand of a given set of scenarios while addressing total expected cost and expected environmental impact minimization. The potential of the approach is illustrated through a case study from the sugar cane industry proposed by Mele et al. (2011).

Processes for recycling or reusing material resources have naturally appeared along with the history of technology development when need, resource scarcity, opportunities, and enabling technologies have combined conveniently with insight, intuition, and fortune. The current pressure for saving resources by efficiently integrating production and consumption processes requires the development of systematic approaches exploring opportunities and determining the best alternatives. While recent research has addressed the problem of directly connecting sources and sinks, we turn our attention to doing so with improved efficiency and at a lower cost. This work addresses the event of a systematic framework for the synthesis and optimization of the industrial process networks required by an economic system aimed at adding value to waste and minimizing the continuous need for non-renewable resources from a circular economy perspective. The systematic framework presented relies upon a novel model formulated as a superstructure that includes the transformation technologies needed for the revaluation of waste, which, in turn, raises resource-sharing opportunities otherwise ignored. The model is flexible enough to address primary uncertainty sources and manage different objectives (e.g., economic and environmental. A case study on hydrocarbons recovery from waste plastic illustrates the capabilities of the model, which proves to be a useful tool for the identification of the most promising routes for waste transformation and the less efficient processes where substantial improvement is required to increase their competitive edge. The methodology is valuable for the multiple actors involving the waste revaluation sector, from private companies to policymakers.

Degradation of bisphenol A (BPA, 0.5 L, 30 mg L-1) was studied by photo-Fenton treatment, while Fenton reagents were variables. The efficiency of the degradation process was evaluated by the reduction of total organic carbon (TOC), the biochemical oxygen demand (BOD), and toxicity. For toxicity analysis, bacterial methods were found infeasible, but the in vitro assay of VERO cells culture was successfully applied. Experiments according to a 22 design of experiments (DOE) with star points and three center points for statistical validity allowed selecting those process conditions (Fe(II) and H2O2 load) that maximized the process performance. Photo-Fenton process effectively eliminated BPA and partly degraded its by-products (residual TOC

A novel scenario-based dynamic negotiation approach is proposed for the coordination of decentralized supply chains under uncertainty. The relations between the involved organizations (client, provider and third parties) and their respective conflicting objectives are captured through a non-zero-sum and non-symmetric roles SBDN negotiation. The client (leader) designs coordination agreements considering the uncertain reaction of the provider (follower) resulting from the uncertain nature of the third parties, which is modeled as a probability of acceptance function. Different negotiation scenarios are studied: (i) cooperative, and (ii) non-cooperative and (iii) standalone cases. The use of the resulting models is illustrated through a case study with different vendors around a “leader” (client) in a decentralized scenario. Although the usual cooperation hypothesis will allow higher overall profit expectations, using the proposed approach it is possible to identify non-cooperative scenarios with high individual profit expectations which are more likely to be accepted by all individual partners.

An important problem to be addressed by diagnostic systems in industrial applications is the estimation of faults with incomplete observations. This work discusses different approaches for handling missing data, and performance of data-driven fault diagnosis schemes. An exploiting classifier and combined methods were assessed in Tennessee-Eastman process, for which diverse incomplete observations were produced. The use of several indicators revealed the trade-off between performances of the different schemes. Support vector machines (SVM) and C4.5, combined with k-nearest neighbourhood (kNN), produce the highest robustness and accuracy, respectively. Bayesian networks (BN) and centroid appear as inappropriate options in terms of accuracy, while Gaussian naive Bayes (GNB) is sensitive to imputation values. In addition, feature selection was explored for further performance enhancement, and the proposed contribution index showed promising results. Finally, an industrial case was studied to assess informative level of incomplete data in terms of the redundancy ratio and generalize the discussion. (C) 2015 Elsevier Ltd. All rights reserved.

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.

The sampling of representative episodic events can be a difficult aspect (Caron et al.,2016, Ou-Yang et al., 2018), especially due to the short duration of episodic occurrences and its possibility of materializing during all hours of the day (Gallego et al., 2008). In this line, the activation (switching on) of the air samplers when an odour episode begins and the switching off of the apparatus at the end of the odour episode, together with the annotation of the time interval when the air sample has been taken, are key aspects. In some cases, the potentially affected people and municipality technicians can activate the samplers. In the same way, a mobile unit may also be handed to the local police in order to follow directionally changing episodes (Roca et al., 2003, Roca 2006, Gallego et al. 2008). However, due to the short time duration of episodes and the potentiality of their occurrence during small hours, this approach to episodic sampling is sometimes not appropriate. Additionally, a bias in sampling representativeness can be added by field personnel (Ou-Yang et al., 2018). In this line, air sensors can be very helpful in peak event identifications (Spinelle et al., 2017, Morawska et al., 2018), and their use to trigger the switching on and off a sampling device is an interesting and innovative technique. On the other hand, the identification of the specific VOCs that generate an odorous episode is an essential point for air quality evaluation. Active sampling of outdoor air using a sorbent-based method (the air is pulled through tubes filled with appropriate sorbents) allows a successful collection of VOCs in air, including a wide range of chemical families, such as alkanes, aromatic hydrocarbons, aldehydes, alcohols, halocarbons, esters, ketones, terpenes, ethers, sulfur and nitrogen compounds, and isocyanates, principally. A good combination of different sorbents allows the qualitative determination of a wide range of C2-C14 VOC, around 200-300 in a Standard urban sample (Ribes et al. 2007, Gallego et al. 2009a). In this manuscript we present the evaluation of the suitability of a commercially available metal oxide semiconductor gas sensor (TGS 2602, Figaro Engineering Inc.) for the activation of a VOCs sampler when episodic events of nuisance/odorous annoyance occur. Concentrations of VOCs in outdoor air during 24 hour and episodic periods were measured simultaneously during 14 days in El Morell, near the Tarragona petrochemical complex. Analysis of VOCs was performed by automatic thermal desorption coupled to capillary gas chromatography/mass spectrometry (TD-GC/MS). VOCs analysis of samples takes place offline, generating a small gap between the sampling and the obtention of the analytical results that can be extended between two to eight days maximum, based on a maximum storage time of samples of one week. VOCs analysis through TD-GC/MS is more time consuming than real time gas analysis (Heninger et al., 2018; Lemaine et al., 2018), however, a good programmation of the sampling and analytical processes allows a rapid obtention of the results. TD-GC/MS analytical methodology, previous to air sampling through multi-sorbent bed tubes, is a very sensitive, selective and reproducible technique, and grants very low limits of detection for the evaluated VOCs, implying a large number of identified compounds within a wide range of VOCs families (Ribes et al., 2007; Gallego et al., 2009a, 2018a), an indispensable aspect for air quality monitoring. In this line, TD-GC/MS has been widely used in outdoor air quality evaluations (Gallego et al., 2009a, 2016, 2017,2018a). This selective methodology allows good chromatographic separation, and identification and quantification of target analytes through their characteristic mass spectrum and quantification ions, respectively (Ribes et al., 2007).

A comparison was made between the relative performance of active and passive sampling methods for the analysis of 1,3-butadiene in outdoor air. Active and passive sampling was conducted using multi-sorbent bed tubes (Carbotrap, Carbopack X, Carboxen 569) and RAD141 Radiello® diffusive samplers (filled with Carbopack X), respectively. Daily duplicate samples of multi-sorbent bed tubes were taken over a period of 14 days (9 + 5 days) at El Morell (Tarragona, Spain), near the petrochemical area. As 1,3-butadiene is a reactive pollutant and can be rapidly oxidized, half of the samplers were equipped with ozone scrubbers. Samples consisted in two tubes connected in series (front and back) to allow the determination of breakthrough. Quadruplicate samples of Radiello® tubes were taken over a period of 14 days (9 days and 5 days), too. During those days, ozone concentration was measured using RAD172 Radiello® samplers. In addition to this, daily duplicate samples of multi-sorbent bed tubes were taken in the city of Barcelona over a period of 8 days. Simultaneously, 4 samples of Radiello® tubes were exposed to outdoor air. Sampling was done throughout June and July 2017. Analysis was performed by thermal desorption coupled with gas chromatography/mass spectrometry. Analytical performance of the two sampling methods was evaluated by describing several quality assurance parameters, with results showing that performances are quite similar. They display low detection limits, good precision, linearity and desorption efficiency, low levels of blank values, and low breakthrough for multi-sorbent bed tubes. However, Radiello® samplers were not able to uptake episodic 1,3-butadiene high concentrations, leading to underestimation of real values. Hence, we can conclude that Radiello® samplers can be used for baseline 1,3-butadiene levels whereas multi-sorbent bed tubes would be advisable when relevant episodes are expected.

This work aims at proposing a novel modeling approach that can be able to identify an approximately unified, causally established coupling between the general photo-Fenton system and the degradation of the arbitrarily chosen organic compounds. The simplified first principles model is based on our idea of consecutive breakage steps, depending on the number of carbon atoms in the molecule to be decomposed. Based on the previous results of combining Direct Computer Mapping based Programmable Structures with experimental design, this paper explains the application of the experimental and computational method for another target, as a first step toward generalization. The model may help experimentation and may also inspire the improvement of practical applications.

The photo-Fenton process is a photochemical process that has proved to be highly efficient in degrading new potentially harmful contaminants. Despite of this, scarce attention has been paid to the development of systematic procedures and optimisation strategies to efficiently operate such a process. The present work aims at investigating the effectiveness of a model-based approach in carrying out the dynamic optimisation of the recipe of a photo-Fenton process, performed in fed-batch mode (reactant dosage). This work has addressed and solved multiple optimisation problems, searching for the optimal hydrogen peroxide (H2O2) dosage profile, and Pareto frontiers have been built accordingly in order to point out the interaction between three main process efficiency parameters, such as processing time, total amount of H2O2 used, and Total Organic Carbon (TOC) removal. Such a study allows mapping the best operating conditions and provides a decision-making oriented overview of the process. An economic study has also been carried out with the aim of finding out the optimal H2O2 dosage profile that guarantees the minimum operating cost under a varying set of operational and environmental constraints, such as TOC removal. This work has adopted and properly adapted the model by Cabrera Reina et al. (2012) in order to describe the evolution of the system under a flexible reactant dosage. Cabrera Reina et al. (2012) proposed a semi-empirical kinetic model to track paracetamol degradation using the photo-Fenton process and experimentally validated this model for a pollutant load range between 4 and 25¿mmol L-1 of TOC (e.g. industrial wastewaters). Dynamic optimisation has been addressed applying a direct simultaneous optimisation method using this extended model. Results have been presented and discussed in regard of optimal H2O2 dosage under both economic and environmental constraints. The model-based optimisation approach has allowed a fast practical recipe adjustment with reduced experimental work.

The electricity generation sector needs to reduce its environmental impact and dependence on fossil fuel, mainly from coal. Biomass is one of the most promising future options to produce electricity, given its potential contribution to climate change mitigation. Even though biomass is an old source of energy, it is not yet a well-established commodity. The use of biomass in large centralised systems requires the establishment of delivery channels to provide the desired feedstock with the necessary attributes, at the right time and place. In terms of time to deployment and cost of the solution, co-combustion/co-gasification of biomass and coal are presented as transition and short-medium term alternatives towards a carbon-neutral energy sector. Hence, there is a need to assess an effective introduction of co-combustion/co-gasification projects in the current electricity production share. The purpose of this work is to review recent steps in Process Systems Engineering towards bringing into reality individualised and ad-hoc solutions, by building a common but adjustable design platform to tailored approaches of biomass-based supply chains. Current solutions and the latest developments are presented and future needs under study are also identified.

© 2017 Elsevier B.V. 1,3-Butadiene is a carcinogenic compound that can be emitted to the atmosphere from several sources, such as the combustion of organic matter or traffic. However, petrochemical industry is one of the main origins of this compound. Several campaigns (2013-2017) were conducted in twelve Catalan urban areas to determine 1,3-butadiene concentrations in outdoor air, five of which were close to Tarragona petrochemical region. 1,3-Butadiene was dynamically sampled in multi-sorbent bed tubes (Carbotrap, Carbopack X and Carboxen 569) using portable pump equipment. The analysis was performed by automatic thermal desorption coupled with capillary gas chromatography/mass spectrometry detector. El Morell, Perafort and Puigdelfi´, located near petrochemical facilities, were the most impacted locations, with average concentrations up to 15±33, 33±41 and 27±39µgm-3, respectively. Maximum 24h concentrations of 125µgm-3 were observed in Puigdelfi´. However, 1,3-butadiene average and maximum concentrations in the Tarragona petrochemical region diminished significantly (p =0.05) in a drastic way (40-80%) since the first monitoring program in 2013. On the other hand, average concentrations in the rest of studied Catalan urban areas generally presented average values below 1µgm-3, ranging from 0.07-1.1µgm-3. Spain does have neither a monitoring strategy nor standard regulations related to 1,3-butadiene. Taking into account that relevant values can be found in locations near petrochemical facilities, a regulation should be implemented as soon as possible, at least in this specific areas.

Microgrids are energy systems that can work independently from the main grid in a stable and self-sustainable way. They rely on energy management systems to schedule optimally the distributed energy resources. Conventionally, the main research in this field is focused on scheduling problems applicable for specific case studies rather than in generic architectures that can deal with the uncertainties of the renewable energy sources. This paper contributes a design and experimental validation of an adaptable energy management system implemented in an online scheme, as well as an evaluation framework to quantitatively assess the enhancement attained by different online energy management strategies. The proposed architecture allows the interaction of measurement, forecasting and optimization modules, in which a generic generation-side mathematical problem is modeled, aiming to minimize operating costs and load disconnections. The whole energy management system has been tested experimentally in a test bench under both grid-connected and islanded mode. Also, its performance has been proved considering severe mismatches in forecast generation and load. Several experimental results have demonstrated the effectiveness of the proposed EMS, assessed by the corresponding average gap with respect to a selected benchmark strategy and ideal boundaries of the best and worst known solutions.

Volatile methyl siloxanes (VMS) were evaluated in ten Catalan urban areas with different industrial impacts, such as petrochemical industry, electrical and mechanical equipment, metallurgical and chemical industries, municipal solid waste treatment plant and cement and food industries, during 2013–2015. 24 h samples were taken with LCMA-UPC pump samplers specially designed in our laboratory, with a flow range of 70 ml min −1 . A sorbent-based sampling method, successfully developed to collect a wide-range of VOC, was used. The analysis was performed by automatic thermal desorption coupled with capillary gas chromatography/mass spectrometry detector. The presented methodology allows the evaluation of VMS together with a wide range of other VOC, increasing the number of compounds that can be determined in outdoor air quality assessment of urban areas. This aspect is especially relevant as a restriction of several VMS (D4 and D5) in consumer products has been made by the European Chemicals Agency and US EPA is evaluating to include D4 in the Toxic Substances Control Act, regarding the concern of the possible effects of these compounds in human health and the environment. ΣVMS concentrations (L2-L5, D3-D6 and trimethylsilanol) varied between 0.3 ± 0.2 μg m −3 and 18 ± 12 μg m −3 , determined in a hotspot area. Observed VMS concentrations were generally of the same order of magnitude than the previously determined in Barcelona, Chicago and Zurich urban areas, but higher than the published from suburban sites and Arctic locations. Cyclic siloxanes concentrations were up to two-three orders of magnitude higher than those of linear siloxanes, accounting for average contributions to the total concentrations of 97 ± 6% for all samples except for the hotspot area, where cyclic VMS accounted for 99.9 ± 0.1%. D5 was the most abundant siloxane in 5 sampling points; however, differing from the generally observed in previous studies, D3 was the most abundant compound in the other 5 sampling points.

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works Microgrids are energy systems that aggregate distributed energy resources, loads, and power electronics devices in a stable and balanced way. They rely on energy management systems to schedule optimally the distributed energy resources. Conventionally, many scheduling problems have been solved by using complex algorithms that, even so, do not consider the operation of the distributed energy resources. This paper presents the modeling and design of a modular energy management system and its integration to a grid-connected battery-based microgrid. The scheduling model is a power generation-side strategy, defined as a general mixed-integer linear programming by taking into account two stages for proper charging of the storage units. This model is considered as a deterministic problem that aims to minimize operating costs and promote self-consumption based on 24-hour ahead forecast data. The operation of the microgrid is complemented with a supervisory control stage that compensates any mismatch between the offline scheduling process and the real time microgrid operation. The proposal has been tested experimentally in a hybrid microgrid at the Microgrid Research Laboratory, Aalborg University.

Standardization is essential for automation. Extensibility, scalability, and reusability are important features for automation software that rely in the efficient modelling of the addressed systems. The work presented here is from the ongoing development of a methodology for semi-automatic ontology construction methodology from technical documents. The main aim of this work is to systematically check the consistency of technical documents and support the improvement of technical document consistency. The formalization of conceptual models and the subsequent writing of technical standards are simultaneously analyzed, and guidelines proposed for application to future technical standards. Three paradigms are discussed for the development of domain ontologies from technical documents, starting from the current state of the art, continuing with the intermediate method presented and used in this paper, and ending with the suggested paradigm for the future. The ISA88 Standard is taken as a representative case study. Linguistic techniques from the semi-automatic ontology construction methodology is applied to the ISA88 Standard and different modelling and standardization aspects that are worth sharing with the automation community is addressed. This study discusses different paradigms for developing and sharing conceptual models for the subsequent development of automation software, along with presenting the systematic consistency checking method

Gloss is a critical issue in many applications in the coating industry. Gloss depends on optical and rheological properties of complex mixtures, and estimating gloss from basic properties is still a challenge. In order to predict the gloss of an industrial thickened-to-application formulation this work presents a gloss-rheology semi empirical-modeling approach based on a gloss excess function and previous work from other authors. A new matt (low gloss) hybrid waterborne polyurethane dispersion composed out of a self-matting agent (A) and a traditional silica-based matting agent (B) has been studied, and the resulting gloss of the mixture has been correlated to pure component gloss values and dynamic viscosity at medium shear rate. Several modeling options have been tested and their goodness of fit has been determined. The most promising options have been selected and validated towards untrained data sets.

Within the context of decision-support tools based on mathematical programming techniques, this contribution presents a procedure for constraints identification and classification. The aim is to manage their transformation to be applied in other decision-making levels and/or scenarios, in order to ensure coherent/integrated decision-making proposals. The procedure is based on combining ontological modelling techniques and procedures to recognize and classify constraint features. As a result, the main patterns related to the different concepts behind the constraints are identified in Conceptual Constraint Domain. Equivalences and differences between already existing formulations are systematized. Consequently, existing formulations can be easily extended, thus significantly reducing the effort required to check their consistency and/or to reuse them in different scenarios.

The present study aims at proposing a kinetic model that can capture the complexity and non-linear nature of the Fenton and photo-Fenton processes in the degradation of a model pollutant. Moreover, the proposed model is also able to account for the effect of the Local Volumetric Rate of Photon Absorption (LVRPA), depending on the radiation field within the annular photoreactor and consequently including the reactor dimensions and lamp characteristics. Paracetamol (PCT) was selected as model pollutant, because it is widely used as antipyretic and analgesic. Three kinetic parameters, accounting for the Fenton-like reaction and the hydroxyl radical attack to hydrogen peroxide and paracetamol, were estimated. Mean Square Error (MSE) and Root Mean Square Error (RMSE) were calculated to validate the model reliability. Fil: Audino, Francesca. Universidad Politécnica de Catalunya; España Fil: Conte, Leandro Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Schenone, Agustina Violeta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Pérez Moya, Montserrat. Universidad Politécnica de Catalunya; España Fil: Graells, Moisès. Universidad Politécnica de Catalunya; España Fil: Alfano, Orlando Mario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina

This paper presents the use of a Conceptual Constraint (CC) Domain to systematize the construction of Decision Making Models (DMMs). The modelling systematics include the integration between the CC Domain and production systems as well as an identification procedure which contains some steps aimed at constraint identification using the CC Domain. The CC Domain consists of different modelling elements such as Conceptual Constraints (generic constraint types), Conceptual Components (pieces of a constraint), and Conceptual Component Elements (pieces of a conceptual component that may be connected to production systems). In this instance, the CC Domain is integrated with the Process Systems Engineering (PSE) Domain as a production system domain. The PSE Domain contains information from the multi-level functional hierarchical in an enterprise and it will be used to cover a wide range of scenarios related to hierarchical integration of DMMs. In addition, an integration step between the CC and PSE Domains is illustrated. The focus of the work is to show how these models should be developed in order to be properly integrated, and how they are used by different functionalities with an identification procedure.

Current supply chain (SC) optimization models deal with material and information flows along few echelons of the SC (“own SC”), minimizing the role of the complex behavior of third parties (raw materials and utilities suppliers, clients, waste and recovery systems, etc.) in the decision-making process of this SC of interest. Third parties are just represented by simplified parameters (capacity, cost, etc.) usually considered constant, but the decisions based on this picture are not adequate when the third parties’ behavior is significantly affected by these decisions or other circumstances, especially when global coordination is attained. In this work, the role of these third parties, which might face different objectives, has been integrated and a solution based on the full SC management problem is proposed. This results on a generic model which may be used to optimize the planning decisions of the multi-product multi-site SC of interest (production/distribution echelons), taking into account the production vs. demand coherence among this SC and the third parties. The features of the proposed model are illustrated using a case study which considers the coordination of a series of resource (energy) generation SCs linked to a production/distribution SC (“SC of interest”). The results show how the behavior of the considered SCs determines the best planning decisions of each organization, which will depend on the way used to coordinated them (e.g. toward less total or individual costs), adding to the PSE science a new point of view which allows all involved organizations to share responsibilities in the system.

A generic tactical model is developed considering third party price policies for the optimization of coordinated and centralized multi-product Supply Chains (SCs). To allow a more realistic assessment of these policies in each marketing situation, different price approximation models to estimate these policies are proposed, which are based on the demand elasticity theory, and result in different model implementations (LP, NLP, and MINLP). The consequences of using the proposed models on the SCs coordination, regarding not only their practical impact on the tactical decisions, but also the additional mathematical difficulties to be solved, are verified through a case study in which the coordination of a production–distribution SC and its energy generation SC is analyzed. The results show how the selection of the price approximation model affects the tactical decisions. The average price approximation leads to the worst decisions with a significant difference in the real total cost in comparison with the best piecewise approximation.