@article{MTMT:34797994,
	title = {PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs},
	url = {https://m2.mtmt.hu/api/publication/34797994},
	author = {Cole, D.L. and Zavala, V.M.},
	doi = {10.1016/j.compchemeng.2024.108679},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {185},
	unique-id = {34797994},
	issn = {0098-1354},
	year = {2024},
	eissn = {1873-4375}
}

@article{MTMT:34753556,
	title = {Automatic initial value generation procedure for nonlinear process models by interval arithmetic based cutting and splitting},
	url = {https://m2.mtmt.hu/api/publication/34753556},
	author = {Bublitz, Saskia and Esche, Erik and Repke, Jens-Uwe},
	doi = {10.1016/j.compchemeng.2024.108672},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {&},
	unique-id = {34753556},
	issn = {0098-1354},
	year = {2024},
	eissn = {1873-4375},
	pages = {108672},
	orcid-numbers = {Bublitz, Saskia/0000-0002-9050-7862; Esche, Erik/0000-0002-2223-1825}
}

@article{MTMT:34747054,
	title = {A virtual screening framework based on the binding site selectivity for small molecule drug discovery},
	url = {https://m2.mtmt.hu/api/publication/34747054},
	author = {Che, Xinhao and Liu, Qilei and Yu, Fang and Zhang, Lei and Gani, Rafiqul},
	doi = {10.1016/j.compchemeng.2024.108626},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {184},
	unique-id = {34747054},
	issn = {0098-1354},
	year = {2024},
	eissn = {1873-4375},
	orcid-numbers = {Che, Xinhao/0009-0000-9450-6130; Zhang, Lei/0000-0002-7519-2858; Gani, Rafiqul/0000-0002-6719-9283}
}

@article{MTMT:34735264,
	title = {Utilizing modern computer architectures to solve mathematical optimization problems: A survey},
	url = {https://m2.mtmt.hu/api/publication/34735264},
	author = {Bernal, Neira D.E. and Laird, C.D. and Lueg, L.R. and Harwood, S.M. and Trenev, D. and Venturelli, D.},
	doi = {10.1016/j.compchemeng.2024.108627},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {184},
	unique-id = {34735264},
	issn = {0098-1354},
	abstract = {Numerical algorithms to solve mathematical optimization problems efficiently are essential to applications in many areas of engineering and computational science. To solve optimization problems of ever-increasing scale and complexity, we need methods that exploit emerging hardware systems. However, the complexities of specific architectures and their impact on performance can be challenging. This article provides an overview of emerging hardware architectures and how they are used to solve mathematical optimization problems. We focus on parallel high-performance computing architectures, which are well-established yet challenging to employ for optimization, as well as digital quantum computing, which has recently gained attention due to its potential for transformative computational performance. Furthermore, we highlight several other emerging hardware architectures that may become relevant for mathematical optimization. We intend for this review to encourage the optimization and process engineering communities to increasingly consider both hardware and software developments in the pursuit of superior computational performance. © 2024},
	keywords = {Optimization; Mathematical programming; Mathematical programming; Quantum computers; Computational efficiency; Optimisations; Computer architecture; Computer hardware; Computational performance; Numerical algorithms; High-performance computing; High-performance computing; Quantum computing; Quantum computing; Engineering science; Hardware architecture; Performance computing; emerging hardware; emerging hardware; Mathematical optimization problems},
	year = {2024},
	eissn = {1873-4375}
}

@article{MTMT:34669265,
	title = {Robust-to-occlusion machine vision model for predicting quality variables with slow-rate measurements},
	url = {https://m2.mtmt.hu/api/publication/34669265},
	author = {Salehi, Yousef and Chiplunkar, Ranjith and Huang, Biao},
	doi = {10.1016/j.compchemeng.2023.108581},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {182},
	unique-id = {34669265},
	issn = {0098-1354},
	keywords = {predictive model; Machine vision; soft sensor; Image inpainting; Multirate data},
	year = {2024},
	eissn = {1873-4375},
	orcid-numbers = {Chiplunkar, Ranjith/0000-0001-5546-0918; Huang, Biao/0000-0001-9082-2216}
}

@article{MTMT:34649520,
	title = {Feasibility/Flexibility-based optimization for process design and operations},
	url = {https://m2.mtmt.hu/api/publication/34649520},
	author = {Tian, Huayu and Jagana, Jnana Sai and Zhang, Qi and Ierapetritou, Marianthi},
	doi = {10.1016/j.compchemeng.2023.108461},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {180},
	unique-id = {34649520},
	issn = {0098-1354},
	keywords = {FLEXIBILITY; Robust optimization; FEASIBILITY; Feasibility-based optimization},
	year = {2024},
	eissn = {1873-4375},
	orcid-numbers = {Ierapetritou, Marianthi/0000-0002-1758-9777}
}

@article{MTMT:34648701,
	title = {Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities},
	url = {https://m2.mtmt.hu/api/publication/34648701},
	author = {Khuat, Thanh Tung and Bassett, Robert and Otte, Ellen and Grevis-James, Alistair and Gabrys, Bogdan},
	doi = {10.1016/j.compchemeng.2024.108585},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {182},
	unique-id = {34648701},
	issn = {0098-1354},
	keywords = {machine learning; Biopharmaceuticals; DOWNSTREAM; upstream; digital twin; soft sensors; Bioprocesses},
	year = {2024},
	eissn = {1873-4375},
	orcid-numbers = {Khuat, Thanh Tung/0000-0002-6456-8530}
}

@article{MTMT:34632016,
	title = {Optimal nonlinear dynamic sparse model selection and Bayesian parameter estimation for nonlinear systems},
	url = {https://m2.mtmt.hu/api/publication/34632016},
	author = {Adeyemo, Samuel and Bhattacharyya, Debangsu},
	doi = {10.1016/j.compchemeng.2023.108502},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {180},
	unique-id = {34632016},
	issn = {0098-1354},
	keywords = {Model selection; Bayesian estimation; CORRELATED NOISE; Noisy data; bound; Sparse system identification; Branch &},
	year = {2024},
	eissn = {1873-4375}
}

@article{MTMT:34612234,
	title = {Economic and environmental optimisation of mixed plastic waste supply chains in Northern Italy comparing incineration and pyrolysis technologies},
	url = {https://m2.mtmt.hu/api/publication/34612234},
	author = {Cristiu, Daniel and d'Amore, Federico and Bezzo, Fabrizio},
	doi = {10.1016/j.compchemeng.2023.108503},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {180},
	unique-id = {34612234},
	issn = {0098-1354},
	abstract = {In the quest for sustainable plastic waste management, understanding economic and environmental implications enables optimal selection of treatment technologies. This study presents a multi-objective mixed integer linear programming framework to optimise the supply chain for mixed plastic waste in Northern Italy. Two technologies are considered: incineration and pyrolysis. Results offer quantitative insights into economic and environmental performance, balancing trade-offs between maximising gross profit and minimising greenhouse gas (GHG) emissions. Economic optimisation favours incineration for treating mixed plastic waste, resulting in the highest gross profit of 115 Meuro per year, and the highest net GHG emissions of about 680 kt CO2eq per year. When the aim is environmental optimisation, pyrolysis is preferred due to its lower GHG emissions of 387 kt of CO2eq per year and yielding a gross profit of 54 Meuro per year. Trade-off Pareto optimal solutions were analysed to identify reasonable trade-off configurations between the two objectives.},
	keywords = {mixed plastic waste; Chemical recycling; Waste-to-Energy; supply chain optimisation; Economic optimisation; Environmental optimisation},
	year = {2024},
	eissn = {1873-4375},
	orcid-numbers = {d'Amore, Federico/0000-0001-8165-3836}
}

@article{MTMT:34609095,
	title = {Integrated supply chain network design and superstructure optimization problem: A case study of microalgae biofuel supply chain},
	url = {https://m2.mtmt.hu/api/publication/34609095},
	author = {Shirazaki, Setayesh and Pishvaee, Mir Saman and Sobati, Mohammad Amin},
	doi = {10.1016/j.compchemeng.2023.108468},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {180},
	unique-id = {34609095},
	issn = {0098-1354},
	abstract = {In recent years, several strategies have been developed and adopted in a bid to manage the biofuel supply chain. In this paper, a two-stage optimization model is proposed for integrated microalgae biofuel supply chain network design and superstructure optimization problems. In the first stage, the design of the carbon capture, utilization, and storage (CCUS) network is taken into account. A robust mixed integer linear programming (RMILP) model is proposed to optimize the strategic CCUS decisions, including the simultaneous selection of emission sources, capture facilitates, Carbon Dioxide (CO2) pipelines, intermediate transportation sites and storage sites, or microalgae cultivation sites. The second stage is dedicated to biorefinery superstructure optimization in order to determine the optimal/promising biorefinery configurations. The presented model is able to handle the inherent uncertainty of critical input parameters. Moreover, the results show that biodiesel production cost cannot compete with current diesel price, but it can be reduced significantly by improving biomass productivity.},
	keywords = {Robust optimization; superstructure optimization; Microalgae biofuel supply chain; Carbon capture utilization and storage (CCUS)},
	year = {2024},
	eissn = {1873-4375},
	orcid-numbers = {Pishvaee, Mir Saman/0000-0001-6389-6308}
}