Circular Economy - A challenge and an opportunity for Process Systems Engineering

Modeling Mechanochemical Depolymerization of PET in Ball-Mill Reactors Using DEM Simulations

Developing efficient and sustainable chemical recycling pathways for consumer plastics is critical for mitigating the negative environmental implications associated with their end-of-life management. Mechanochemical depolymerization reactions have recently garnered great attention, as they are recognized as a promising solution for solvent-free transformation of polymers to monomers in the solid state. To this end, physics-based models that accurately describe the phenomena within ball mills are necessary to facilitate the exploration of operating conditions that would lead to optimal performance. Motivated by this, in this paper we develop a mathematical model that couples results from discrete element method (DEM) simulations and experiments to study mechanically-induced depolymerization. The DEM model was calibrated and validated via video experimental data and computer vision algorithms. A systematic study on the influence of the ball-mill operating parameters revealed a direct relationship between the operating conditions of the vibrating milling vessel and the total energy supplied to the system. Moreover, we propose a linear correlation between the high-fidelity DEM simulation results and experimental monomer yield data for poly(ethylene terephthalate) depolymerization, linking mechanical and energetic variables. Finally, we train a reduced-order model to address the high computational cost associated with DEM simulations. The predicted working variables are used as inputs to the proposed mathematical expression which allows for the fast estimation of monomer yields.

Lignocellulosic biorefineries: A multiscale approach for resource exploitation

Biomass can become the source for chemicals towards a sustainable production system. However, the challenges it presents such as the variety of species, their widespread and sparse availability, and the expensive transportation claims for an integrated approach to design the novel production system. Multiscale approaches have not been properly extended to biorefineryes design and deployment, due to the comprehensive experimental and modelling work they require. A systems perspective provides the systematic framework to analyze the availability and composition of raw materials across regions, how that affects process design, the portfolio of products that can be obtained by evaluating the strong link between the biomass features and the process design. The use of lignocellulosic materials requires for a multidisciplinary work, that must lead to new process engineers with technical competences in biology, biotechnology but also process engineering, mathematics, computer science and social sciences towards a sustainable process/chemical industry.

A decision-making framework for the optimal design of renewable energy systems under energy-water-land nexus considerations

The optimal allocation of land for energy generation is of emergent concern due to an increasing demand for renewable power capacity, land scarcity, and the diminishing supply of water. Therefore, economically, socially and environmentally optimal design of new energy infrastructure systems require the holistic consideration of water, food and land resources. Despite huge efforts on the modeling and optimization of renewable energy systems, studies navigating the multi-faceted and interconnected food-energy-water-land nexus space, identifying opportunities for beneficial improvement, and systematically exploring interactions and trade-offs are still limited. In this work we present the foundations of a systems engineering decision-making framework for the trade-off analysis and optimization of water and land stressed renewable energy systems. The developed framework combines mathematical modeling, optimization, and data analytics to capture the interdependencies of the nexus elements and therefore facilitate informed decision making. The proposed framework has been adopted for a water-stressed region in south-central Texas. The optimal solutions of this case study highlight the significance of geographic factors and resource availability on the transition towards renewable energy generation.

Investigating the effects of the outbreak of COVID-19 on perishable food supply chains: an empirical study using PLS-SEM

Purpose

At the end of 2019, the first case of the Corona Virus Disease (COVID-19) was reported in Wuhan city of China. The disease was declared a pandemic without imagining the magnitude of damage currently caused in all branches of the economy. One of the most affected sectors was food and mostly perishable food (PF), which are more susceptible to environmental conditions. Thus, the research examines the effect of the COVID-19 outbreak on Ecuador's perishable food supply chains (PFSCs) during the pandemic. It contributes to new results on the special issue (SI) PFSC response to event risk and uncertainty, such as those that generated the pandemic.

Design/methodology/approach

The data used are from primary information sources, which were collected through a questionnaire. The questionnaire was applied to 298 companies belonging to the sector, and later the information was processed through partial least squares structural equation model. The convergent validity, discriminate and robustness tests provide arguments for the suitability of the model. Therefore, the findings are reliable and valid for the adequate measures to improve the PFSC due to a COVID-19 outbreak.

Findings

The results show that the perception of personal risk (PPR) produced by COVID-19 has caused the companies of the PFSC to adopt preventive policies (PO) to avoid contagion and guarantee the operation of the companies. In addition, the PPR has been responsible for the alterations in the demand and price (DP) of PF. Next, PO and DP have a significant effect on PFSC, which shows the evidence favouring the malfunction of PFSC operations due to anti-contagion PO, the mismatch of DP. On the contrary, circular economy practices contribute to the excellent performance of the PFSC. Finally, the research suggests some policy implications to consider in improving the PFSC.

Originality/value

This study is the first to be carried out in Ecuador's country on the PFSC; its contribution is unprecedented and makes it a road-map to be considered to guarantee the correct functioning of the PFSCs, and it will provide policymakers with valid elements to design efficient PFSCs that better respond to unforeseen events and uncertainties. Future research will focus on analysing the management of PF consumption in Ecuador during the pandemic.

A systems engineering framework for the optimization of food supply chains under circular economy considerations

The current linear "take-make-waste-extractive" model leads to the depletion of natural resources and environmental degradation. Circular Economy (CE) aims to address these impacts by building supply chains that are restorative, regenerative, and environmentally benign. This can be achieved through the re-utilization of products and materials, the extensive usage of renewable energy sources, and ultimately by closing any open material loops. Such a transition towards environmental, economic and social advancements requires analytical tools for quantitative evaluation of the alternative pathways. Here, we present a novel CE system engineering framework and decision-making tool for the modeling and optimization of food supply chains. First, the alternative pathways for the production of the desired product and the valorization of wastes and by-products are identified. Then, a Resource-Task-Network representation that captures all these pathways is utilized, based on which a mixed-integer linear programming model is developed. This approach allows the holistic modeling and optimization of the entire food supply chain, taking into account any of its special characteristics, potential constraints as well as different objectives. Considering that typically CE introduces multiple, often conflicting objectives, we deploy here a multi-objective optimization strategy for trade-off analysis. A representative case study for the supply chain of coffee is discussed, illustrating the steps and the applicability of the framework. Single and multi-objective optimization formulations under five different coffee-product demand scenarios are presented. The production of instant coffee as the only final product is shown to be the least energy and environmental efficient scenario. On the contrary, the production solely of whole beans sets a hypothetical upper bound on the optimal energy and environmental utilization. In both problems presented, the amount of energy generated is significant due to the utilization of waste generated for the production of excess energy.

Process modelling and life cycle assessment coupled with experimental work to shape the future sustainable production of chemicals and fuels

Meeting the sustainable development goals and carbon neutrality targets requires transitioning to cleaner products, which poses significant challenges to the future chemical industry. Identifying alternative pathways to cover the growing demand for chemicals and fuels in a more sustainable manner calls for close collaborative programs between experimental and computational groups as well as new tools to support these joint endeavours. In this broad context, we here review the role of process systems engineering tools in assessing and optimising alternative chemical production patterns based on renewable resources, including renewable carbon and energy. The focus is on the use of process modelling and optimisation combined with life cycle assessment methodologies and network analysis to underpin experiments and generate insight into how the chemical industry could optimally deliver chemicals and fuels with a lower environmental footprint. We identify the main gaps in the literature and provide directions for future work, highlighting the role of PSE concepts and tools in guiding the future transition and complementing experimental studies more effectively.

Analyzing Barriers of Circular Food Supply Chains and Proposing Industry 4.0 Solutions

The concept of the circular economy (CE) has gained importance worldwide recently since it offers a wider perspective in terms of promoting sustainable production and consumption with limited resources. However, few studies have investigated the barriers to CE in circular food supply chains. Accordingly, this paper presents a systematic literature review of 136 papers from 2010 to 2020 from WOS and Scopus databases regarding these barriers to understand CE implementation in food supply chains. The barriers are classified under seven categories: “cultural”, “business and business finance”, “regulatory and governmental”, “technological”, “managerial”, “supply-chain management”, “knowledge and skills”. The findings show the need to identify barriers preventing the transition to CE. The findings also indicate that these challenges to CE can be overcome through Industry 4.0, which includes a variety of technologies, such as the Internet of Things (IoT), cloud technologies, machine learning, and blockchain. Specifically, machine learning can offer support by making workflows more efficient through the forecasting and analytical capabilities of food supply chains. Blockchain and big data analytics can provide the necessary support to establish legal systems and improve environmental regulations since transparency is a crucial issue for taxation and incentives systems. Thus, CE can be promoted via adequate laws, policies, and innovative technologies.

Multiobjective Optimization of Mixed-Integer Linear Programming Problems: A Multiparametric Optimization Approach

Industrial process systems need to be optimized, simultaneously satisfying financial, quality and safety criteria. To meet all those potentially conflicting optimization objectives, multiobjective optimization formulations can be used to derive optimal trade-off solutions. In this work, we present a framework that provides the exact Pareto front of multiobjective mixed-integer linear optimization problems through multiparametric programming. The original multiobjective optimization program is reformulated through the well-established ϵ-constraint scalarization method, in which the vector of scalarization parameters is treated as a right-hand side uncertainty for the multiparametric program. The algorithmic procedure then derives the optimal solution of the resulting multiparametric mixed-integer linear programming problem as an affine function of the ϵ parameters, which explicitly generates the Pareto front of the multiobjective problem. The solution of a numerical example is analytically presented to exhibit the steps of the approach, while its practicality is shown through a simultaneous process and product design problem case study. Finally, the computational performance is benchmarked with case studies of varying dimensionality with respect to the number of objective functions and decision variables.

Biomass Hydrothermal Carbonization: Markov-Chain Monte Carlo Data Analysis and Modeling

This paper introduces Bayesian statistical methods for studying the kinetics of biomass hydrothermal carbonization. Two simple, specially developed computer programs implement Markov-chain Monte Carlo methods to illustrate these techniques' potential, long since established in other areas of chemical reaction engineering. A range of experimental data, both from this study and the literature, test the soundness of a Bayesian approach to modeling biomass hydrothermal carbonization kinetics. The first program carries out parameter estimations and performs better or equal than the traditional deterministic methods (R2 as high as 0.9998). For three out of the 22 datasets, the program detected the global minima of the parameter space, while the deterministic least-square found local values. The second program uses Gillespie's algorithm for the statistical simulation of the reactions occurring in hydrothermal carbonization. Comparing six basic kinetic models with literature data tested the stochastic simulation as a tool for assessing biomass conversion reaction networks rapidly. Among the simple models discussed, reaction scheme 3 fitted better to the experimental data (R2 > 0.999). The proposed approach is worth extending to more complex, time-consuming computer models and could support other techniques for studying hydrothermal conversions.

Process assessment, integration and optimisation: The path towards cleaner production

This contribution starts from the broad perspective of the global material cycles, analysing the main resource and pollution issues world-wide from the viewpoint of the disturbances to these cycles caused by human activities. The issues are analysed in the light of the currently developing COVID-19 pandemic with the resulting behavioural and business pattern changes. It has been revealed in the analysis of previous reviews that there is a need for a more comprehensive analysis of the resource and environmental impact contributions by industrial and urban processes, as well as product supply chains. The review discusses the recent key developments in the areas of Process Integration and Optimisation, the assessment and reduction of process environmental impacts, waste management and integration, green technologies. That is accompanied by a review of the papers in the current Virtual Special Issue of the Journal of Cleaner Production which is dedicated to the extended articles developed on the basis of the papers presented at the 22nd Conference on Process Integration for Energy Saving and Pollution Reduction. The follow-up analysis reveals significant advances in the efficiency and emission cleaning effects of key processes, as well as water/wastewater management and energy storage. The further analysis of the developments identifies several key areas for further research and development - including increases of the safety and robustness of supply networks for products and services, increase of the resources use efficiency of core production and resource conversion processes, as well as the emphasis on improved product and process design for minimising product wastage.

Process Design and Sustainable Development—A European Perspective

This paper describes the state of the art and future opportunities for process design and sustainable development. In the Introduction, the main global megatrends and the European Union’s response to two of them, the European Green Deal, are presented. The organization of professionals in the field, their conferences, and their publications support the two topics. A brief analysis of the published documents in the two most popular databases shows that the environmental dimension predominates, followed by the economic one, while the social pillar of sustainable development is undervalued. The main design tools for sustainability are described. As an important practical case, the European chemical and process industries are analyzed, and their achievements in sustainable development are highlighted; in particular, their strategies are presented in more detail. The conclusions cover the most urgent future development areas of (i) process industries and carbon capture with utilization or storage; (ii) process analysis, simulation, synthesis, and optimization tools, and (iii) zero waste, circular economy, and resource efficiency. While these developments are essential, more profound changes will be needed in the coming decades, such as shifting away from growth with changes in habits, lifestyles, and business models. Lifelong education for sustainable development will play a very important role in the growth of democracy and happiness instead of consumerism and neoliberalism.

Smart Porous Multi-Stimulus Polysaccharide-Based Biomaterials for Tissue Engineering

Recently, tissue engineering and regenerative medicine studies have evaluated smart biomaterials as implantable scaffolds and their interaction with cells for biomedical applications. Porous materials have been used in tissue engineering as synthetic extracellular matrices, promoting the attachment and migration of host cells to induce the in vitro regeneration of different tissues. Biomimetic 3D scaffold systems allow control over biophysical and biochemical cues, modulating the extracellular environment through mechanical, electrical, and biochemical stimulation of cells, driving their molecular reprogramming. In this review, first we outline the main advantages of using polysaccharides as raw materials for porous scaffolds, as well as the most common processing pathways to obtain the adequate textural properties, allowing the integration and attachment of cells. The second approach focuses on the tunable characteristics of the synthetic matrix, emphasizing the effect of their mechanical properties and the modification with conducting polymers in the cell response. The use and influence of polysaccharide-based porous materials as drug delivery systems for biochemical stimulation of cells is also described. Overall, engineered biomaterials are proposed as an effective strategy to improve in vitro tissue regeneration and future research directions of modified polysaccharide-based materials in the biomedical field are suggested.

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

Purpose

The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths.

Design/methodology/approach

Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities.

Findings

This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions.

Research limitations/implications

It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities.

Social implications

Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities.

Originality/value

This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

Thermodynamics-Based Process Sustainability Evaluation

This article considers the problem of the evaluation of the sustainability of heterogeneous process systems, which can have different areas of focus: from single process operations to complete supply chains. The proposed method defines exergy-based concepts to evaluate the assets, liabilities, and the exergy footprint of the analysed process systems, ensuring that they are suitable for Life Cycle Assessment. The proposed concepts, evaluation framework and cumulative Exergy Composite Curves allow the quantitative assessment of process systems, including alternative solutions. The provided case studies clearly illustrate the applicability of the method and the close quantitative relationship between the exergy profit and the potential sustainability contribution of the proposed solutions. The first case study demonstrates how the method is applied to the separation and reuse of an acetic-acid-containing waste stream. It is shown that the current process is not sustainable and needs substantial external exergy input and deeper analysis. The second case study concerns Municipal Solid Waste treatment and shows the potential value and sustainability benefit that can be achieved by the extraction of useful chemicals and waste-to-energy conversion. The proposed exergy footprint accounting framework clearly demonstrates the potential to be applied to sustainability assessment and process improvement while simultaneously tracking different kinds of resources and impacts.