Article
Environmental Sciences
Sourena Rahmani, Alireza Goli
Summary: The excessive consumption of fossil fuels has led to environmental damage, prompting the global community to search for a suitable alternative. Biodiesel, a clean and eco-friendly fuel, has emerged as one viable option. To promote mass-level production of biodiesel, a sustainable supply chain network is necessary. This study proposes a mathematical model and scenario-based robust optimization approach to design such a network, resulting in achievable and efficient production and distribution of biodiesel fuel.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Engineering, Industrial
Zoe Krug, Romain Guillaume, Olga Battaia
Summary: The implementation of reverse supply chains has shown benefits in job creation, raw material savings, and income generation, but designing them involves handling uncertainties and risks. A new risk/opportunity approach has been proposed to give more weight to positive scenarios, guiding decision-making processes in distinguishing between zones of risk and opportunity. Different methods have been developed to compute the optimal solution for lexicographic R-*(LexiR(*)) criterion in handling scenario sets and decision-making processes in reverse supply chain management.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Engineering, Chemical
Arash Bazyar, Naeme Zarrinpoor, Amir Safauian
Summary: The study proposes a multi-objective mathematical model for designing a natural gas supply chain network focusing on sustainable development goals. The model aims to minimize supply chain costs economically, reduce environmental impact, and consider social factors. Uncertainty in parameters is taken into account, and the model is evaluated using a real case study, demonstrating its efficiency and the impact of uncertainty on system costs.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2021)
Article
Engineering, Chemical
Congqin Ge, Lifeng Zhang, Zhihong Yuan
Summary: This paper proposes a hybrid stochastic and distributionally robust optimization approach to tackle uncertainty and disruptions in the closed-loop supply chain network. By customizing an algorithm, large-scale mixed integer linear programming problems can be solved efficiently. Computational experiments demonstrate the advantages of this approach in terms of costs and variances.
Article
Computer Science, Artificial Intelligence
Huili Pei, Hongliang Li, Yankui Liu
Summary: This paper addresses the issue of demand uncertainty in dual-channel supply chain, proposing a novel uncertainty distribution set to model ambiguous demand distribution and developing a distributionally robust bilevel optimization framework for capital-constrained scenarios. Different financing strategies and their impact on manufacturers are investigated, revealing that demand ambiguity and equity ratio can influence the manufacturer's equilibrium financing strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Chemical
Oluwadare Badejo, Marianthi Ierapetritou
Summary: In this study, a two-stage stochastic programming model is proposed to address a four-echelon supply chain problem with disruptions at nodes, transportation modes, and operational uncertainties in uncertain demands. The first stage decisions include supplier choice, capacity levels, inventory levels, transportation mode selection, and shipment decisions for a certain period. The second stage anticipates the cost of meeting future demands based on the first stage decision. Comparison with a multi-period deterministic model shows that the stochastic model provides a better first stage decision against future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain networks considering disruptions and operational uncertainties.
Article
Green & Sustainable Science & Technology
Omid Abdolazimi, Farzad Bahrami, Davood Shishebori, Majid Alimohammadi Ardakani
Summary: The paper proposes a multi-objective closed-loop supply chain network model formulated as a mixed-integer linear programming model to minimize total costs, maximize on-time delivery, and maximize quality. The model considers supplier selection, parameter uncertainties, and employs a robust optimization approach to deal with uncertainty. Four exact methods are used to solve the objective functions, with the SLGP method chosen as the most effective, as demonstrated by its minimal deviations compared to other methods, with outputs presented to illustrate its performance.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2022)
Article
Engineering, Industrial
Zoe Krug, Romain Guillaume, Olga Battaia
Summary: Implementing reverse supply chains (RSC) can bring benefits such as reducing pollution and creating new jobs, but it also comes with risks and unpredictable outcomes. This paper presents a model to help managers evaluate risks and opportunities, while maximizing total network profit when managing the reverse flow of end-of-life products in an existing supply chain. The decision-making process incorporates uncertainty and criteria to select the final solution.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Florencia Lujan Garcia-Castro, Ruben Ruiz-Femenia, Raquel Salcedo-Diaz, Jose A. Caballero
Summary: This paper aims to improve the modeling of supply chain designs by considering correlated uncertainty among multiple parameters. A new methodology is presented to generate forecasts for historically correlated time series and applied to energy and carbon prices. These scenarios are then used to obtain a three-echelon supply chain design in Europe.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Artificial Intelligence
Honghua Shi, Yaodong Ni
Summary: This paper focuses on the problems faced by supply chain resilience design and proposes two uncertain programming models to address the risks in the supply chain. By controlling costs and handling uncertainty, these models can help make better decisions. The proposed models are validated through examples and a practical case, demonstrating their effectiveness and feasibility.
Review
Green & Sustainable Science & Technology
Nan Chen, Jianfeng Cai, Yanran Ma, Wenting Han
Summary: This study reviews the literature in the field of green supply chain management (GSCM) under uncertainty published from 2011 to 2020, analyzing and classifying 198 articles. It focuses on bibliometric analysis, identifying uncertainty factors, research directions, and management methods in the domain, while also proposing research agendas for the future.
INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY
(2022)
Article
Green & Sustainable Science & Technology
Mahsa Yadegari, Hadi Sahebi, Sobhan Razm, Jalal Ashayeri
Summary: This research develops an optimization model for designing a hybrid energy supply network that addresses economic, environmental, and social objectives in order to tackle the challenges of sustainable development. The model considers uncertainties and is validated using data from Iran, providing decision-makers with Pareto optimal solutions.
Article
Green & Sustainable Science & Technology
Changqiang Guo, Hao Hu, Shaowen Wang, Luis F. Rodriguez, K. C. Ting, Tao Lin
Summary: This article discusses the challenges posed by spatiotemporal uncertainties in crop residue collection and proposes a multiperiod stochastic programming model to support decision-making in biomass-to-biofuel supply chain networks (BSCN). By comparing the economic performance of different models, it is found that the SP model achieves higher cost savings in the validation period and demonstrates stronger robustness to uncertainty compared to the DPES model.
Article
Energy & Fuels
Naeme Zarrinpoor, Aida Khani
Summary: This research proposes a novel multi-objective model for designing a biofuel supply chain, taking into account financial decisions, sustainability, international suppliers, and markets. The model utilizes fuzzy best-worst method and fuzzy TOPSIS for supplier selection and incorporates a possibilistic programming approach to deal with parameter uncertainties. The suggested model has a notable impact on improving sustainable facets and can be implemented by governments and legislators.
BIOMASS CONVERSION AND BIOREFINERY
(2022)
Article
Management
Qi Lin, Qiuhong Zhao, Benjamin Lev
Summary: This paper examines production and procurement decisions in influenza vaccine supply chains, highlighting the inefficiency that exists in both centralized and decentralized systems. A procurement strategy is proposed to improve coordination in the supply chain, with the aim of enhancing efficiency.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Engineering, Chemical
Federico d'Amore, Fabrizio Bezzo
Article
Biotechnology & Applied Microbiology
Pierantonio Facco, Simeone Zomer, Ruth C. Rowland-Jones, Douglas Marsh, Paloma Diaz-Fernandez, Gary Finka, Fabrizio Bezzo, Massimiliano Barolo
BIOCHEMICAL ENGINEERING JOURNAL
(2020)
Article
Green & Sustainable Science & Technology
Federico D'Amore, Matteo Carmelo Romano, Fabrizio Bezzo
Summary: Carbon capture and storage plays a key role in decarbonising the power and industry sectors. Optimisation of the supply chain is crucial for the widespread implementation of these technologies. A Europe-wide carbon capture and storage supply chain has been optimised using a mixed integer linear programming framework.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Green & Sustainable Science & Technology
Federico d'Amore, Matteo Carmelo Romano, Fabrizio Bezzo
Summary: A multi-echelon mixed integer linear programming model is developed to optimize the design of carbon capture and storage supply chains from industrial sources in Europe, with economic optimization based on country-wise or Europe-wide carbon reduction targets. This study shows that removing 50% of industrial CO2 emissions in each country costs 60.5 euro /t, with variations in costs based on different storage restrictions and reduction targets. Despite variations in transportation methods, setting a Europe-wide reduction target leads to a slight decrease in costs across all scenarios.
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL
(2021)
Article
Engineering, Chemical
Federico Zuecco, Matteo Cicciotti, Pierantonio Facco, Fabrizio Bezzo, Massimiliano Barolo
Summary: The article presents a structured methodology to assist troubleshooting plant-wide batch processes in data-rich environments utilizing multivariate statistical techniques. By analyzing the last unit where the fault occurs and moving backwards through the units, the origin of the fault can be isolated and identified using multivariate statistical models and engineering judgment, leading to significant productivity improvements.
Article
Pharmacology & Pharmacy
Francesca Cenci, Gabriele Bano, Charalampos Christodoulou, Yuliya Vueva, Simeone Zomer, Massimiliano Barolo, Fabrizio Bezzo, Pierantonio Facco
Summary: This study examines the impact of powder lubricant selection on tablet manufacturing in direct compression solid dosage production, proposing a new method to reduce experimental workload and successfully achieve a 60-70% reduction in experimental effort.
INTERNATIONAL JOURNAL OF PHARMACEUTICS
(2022)
Article
Biochemistry & Molecular Biology
Luca Zanella, Pierantonio Facco, Fabrizio Bezzo, Elisa Cimetta
Summary: The classification of high dimensional gene expression data is crucial for the development of effective diagnostic and prognostic tools. This study compared different combinations of feature selectors and classification learning algorithms, and evaluated their performance through empirical studies. The results showed that the quality of data related to the target classes is essential for successful classification of cancer phenotypes, and simple, well-established feature selectors combined with optimized classifiers can achieve good performance without the need for complicated and computationally demanding methods.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biotechnology & Applied Microbiology
Gianmarco Barberi, Antonio Benedetti, Paloma Diaz-Fernandez, Daniel C. Sevin, Johanna Vappiani, Gary Finka, Fabrizio Bezzo, Massimiliano Barolo, Pierantonio Facco
Summary: This study aims to use machine learning to integrate process and biological information for early prediction of cell line performance and explore the relationship with metabolic mechanisms, as well as identify biomarkers to accelerate the selection of high performing cell lines.
METABOLIC ENGINEERING
(2022)
Article
Engineering, Chemical
Christopher Castaldello, Pierantonio Facco, Fabrizio Bezzo, Christos Georgakis, Massimiliano Barolo
Summary: This study explores an alternative optimization route by using a surrogate model to overcome the numerical challenges faced by computationally demanding knowledge-driven models in industrial implementation. By employing the Design of Dynamic Experiments, the surrogate model is estimated for a freeze-drying process in the pharmaceutical industry. The results show that the proposed data-driven route calculates the optimum significantly faster than the knowledge-driven model, with a slight sacrifice in the computed value of the process performance.
Article
Computer Science, Interdisciplinary Applications
Margherita Geremia, Fabrizio Bezzo, Marianthi G. Ierapetritou
Summary: In this study, a novel workflow combining different mathematical tools is proposed for comprehensive feasibility analysis, including determination of feasible space, required sampling points, and selection of surrogate models. Test results demonstrate the effectiveness of this method in identifying complex feasible regions.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Engineering, Chemical
Francesco Sartori, Pierantonio Facco, Federico Zuecco, Fabrizio Bezzo, Massimiliano Barolo
Summary: This paper proposes an optimal indicator-variable approach for phase partitioning and trajectory synchronization in uneven-length multiphase batch processes. The approach automatically performs partitioning into phases and selection of the most appropriate indicator variable, maximizing the performance of the product quality assessment model being developed.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Daniel Cristiu, Federico d'Amore, Paolo Mocellin, Fabrizio Bezzo
Summary: This study develops a multi-objective mixed integer linear programming model framework to optimize the design of carbon capture and sequestration infrastructure in Italy. The results show that the optimal solution considering seismic risk increases the total cost, and offshore sequestration alone also leads to cost increase.
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL
(2023)
Article
Engineering, Chemical
Elia Arnese-Feffin, Pierantonio Facco, Daniele Turati, Fabrizio Bezzo, Massimiliano Barolo
Summary: In this study, a hybrid modeling strategy is proposed to characterize reversible and irreversible fouling in multi-module biorefinery membrane separation systems. The combination of a data-driven model and a knowledge-driven model allows monitoring of membrane fouling and valuable insight to be obtained through simple visual inspection.
CHEMICAL ENGINEERING SCIENCE
(2024)
Article
Engineering, Chemical
Christopher Castaldello, Alessio Gubert, Eleonora Sforza, Pierantonio Facco, Fabrizio Bezzo
Summary: A soft sensing approach based on multivariate image regression is proposed for quantifying biomass concentration and Chlorophyll a content per cell during microalgae culture, showing promising accuracy compared to traditional measurement methods.
Article
Engineering, Chemical
Riccardo De-Luca, Gabriele Bano, Emanuele Tomba, Fabrizio Bezzo, Massimiliano Barolo
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2020)
Article
Agricultural Engineering
G. Grasa, I. Martinez, R. Murillo
Summary: Gasification kinetics of six chars from residual origin were studied under relatively low temperature, low CO2, and high H2O partial pressures. The Random Pore Model (RPM) showed the best fit to experimental results, but the selection of the reaction model depended on the ash composition, specifically the presence of alkali and alkaline earth metals. Chars with ash content higher than 30% wt. were modeled with the RPM model, while chars with the highest K/Si ratio required modified versions of the RPM to accurately predict reaction rates. Textural properties played a key role in determining reaction parameters, such as the pre-exponential factor and activation energy, for chars with similar ash content and composition.
BIOMASS & BIOENERGY
(2024)
Review
Agricultural Engineering
V. Godvin Sharmila, Surya Prakash Shanmugavel, J. Rajesh Banu
Summary: Proper treatment and disposal of biomass waste is crucial to prevent environmental deposition and its negative impacts. Biofuel has emerged as a potential alternative to fossil fuels, reducing carbon emissions and meeting global energy demands. This review examines different biomass waste conversion techniques and explores the production of biofuels with zero carbon emissions. Research on anaerobic treatment, metabolic engineering, and artificial intelligence has been conducted to enhance biofuel production efficiency.
BIOMASS & BIOENERGY
(2024)
Review
Agricultural Engineering
Selvakumar Periyasamy, Adane Asefa Adego, P. Senthil Kumar, G. G. Desta, T. Zelalem, V. Karthik, J. Beula Isabel, Mani Jayakumar, Venkatesa Prabhu Sundramurthy, Gayathri Rangasamy
Summary: Valorizing agricultural waste into valuable products is crucial for environmental protection and bioeconomy advancement. Preprocessing of agricultural waste is a critical step to convert free carbohydrate molecules for final conversion, and factors such as biomass nature, feed loading, pH, temperature, and time influence the process. This review provides comprehensive information on agricultural waste availability, preprocessing techniques, and factors influencing performance.
BIOMASS & BIOENERGY
(2024)
Article
Agricultural Engineering
Aqueel Ahmad, Ashok Kumar Yadav, Achhaibar Singh, Dinesh Kumar Singh
Summary: The study focuses on predicting and optimizing the yield of biogas production in an anaerobic digester using co-digestion. Experimental data was used to develop a machine learning-based prognostic model, and the Response Surface Methodology (RSM) was employed to optimize the parameters. The results demonstrate that RSM coupled with machine learning is an effective technique for modeling, predicting, and optimizing biogas production yield.
BIOMASS & BIOENERGY
(2024)
Article
Agricultural Engineering
Yijing Zhong, Wenxiang Zhai, Xinli Wei
Summary: This paper studies the thermal stability and decomposition of cork materials with and without silica aerogel filler. The results show that the decomposition is inhibited and the pyrolysis is significantly reduced with the addition of silica aerogel. This finding suggests that silica aerogel-infused cork may be a promising raw material for biofuel production with reduced environmental pollution.
BIOMASS & BIOENERGY
(2024)
