Review
Chemistry, Analytical
Debbie van der Burg, Leila Josefsson, Asa Emmer, Cari E. Sanger van de Griend
Summary: The biopharmaceutical market is rapidly growing, and effective monitoring of the biopharmaceutical process is crucial for affordable and reliable therapeutics. Capillary electrophoresis (CE) has proven to be a valuable technique for analyzing various aspects of the process, including product concentration, quality attributes, impurities, and nutrients. CE offers many benefits, such as handling complex matrices, high resolving power, minimal sample preparation, rapid analysis, and low solvent and sample consumption.
TRAC-TRENDS IN ANALYTICAL CHEMISTRY
(2023)
Article
Energy & Fuels
Jiangjiang Wang, Tian Lei, Xiaoling Qi, Lei Zhao, Zhijian Liu
Summary: This paper presents a two-layer optimization model for determining the optimal installation nodes and capacities of electric energy storage (EES) and thermal energy storage (TES) in integrated energy networks. By utilizing storage units to convert electric power into heat, the economic performance and utilization rate of renewable energy sources are improved.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Engineering, Electrical & Electronic
Xin He, Yik-Chung Wu
Summary: The paper introduces a new optimization methodology, called the set squeezing procedure, for solving chance-constrained programming problems under continuous uncertainty distribution. Through novel analyses of the local structure of the feasible set, the generally intractable chance constraints and unknown convexity are addressed. The set squeezing procedure is proved to converge and local optimality is guaranteed under mild conditions, with efficient algorithms derived for widely used quadratically perturbed constraints.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Feifan Lin, Xiaojun Zhou, Chaojie Li, Tingwen Huang, Chunhua Yang
Summary: This article presents a fuzzy chance-constrained dynamic optimization method for modeling uncertain and dynamic industrial production processes. Using credibility theory to quantify the fuzzy uncertainty level of constraints, an improved fuzzy simulation technique and a data-driven state transition algorithm based on deep neural networks are proposed to solve the FCCDO problem, achieving stable, global, and robust optimization performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Seyed-Ali Mirnezami, Reza Tavakkoli-Moghaddam, Reza Shahabi-Shahmiri, Mohammad Ghasemi
Summary: This paper investigates a multi-mode resource-constrained project scheduling problem (MRCPSP) with multiple skills. A new multi-objective mixed-integer linear programming (MILP) model with three objective functions is proposed to minimize project makespan, total resource costs, and total project risk. The effectiveness of the proposed model is demonstrated through a real-world construction project. The results show that the proposed lexicographic optimization algorithm outperforms the AUGMECON method in all problem instances.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Review
Chemistry, Medicinal
Tayfun Tanir, Marvin Orellana, Aster Escalante, Carolina Moraes de Souza, Michael S. Koeris
Summary: This article provides an overview of the process and challenges of phage manufacturing, including cell line development, upstream and downstream processing, as well as the additional opportunities presented by engineered bacteriophages.
Article
Thermodynamics
R. Cao, G. H. Huang, J. P. Chen, Y. P. Li, C. Y. He
Summary: The study reveals that implementing cooperative carbon dioxide emission strategies in the region can reduce system costs, especially in higher water resource risk scenarios. Additionally, water consumption increases as the constraint-violation level decreases.
Article
Energy & Fuels
Yuqi Zhou, Wenbin Yu, Shanying Zhu, Bo Yang, Jianping He
Summary: This paper investigates the energy management problem of an integrated retailer in multi-energy systems considering uncertainties. It proposes a model with chance constraints and risk sensitive cost, and utilizes both analytical and robust optimization methods to find solutions. The simulation results show that the proposed method outperforms existing techniques by producing less conservative and more effective results for energy management under uncertainties.
Article
Engineering, Chemical
Bo Liu, Yufei Wang, Xiao Feng
Summary: This paper investigates the optimization of circulating cooling water systems under uncertain circumstances, proposing a model based on chance constrained programming method. An algorithm using Monte Carlo method is developed to solve the model, which aims to minimize total cost and achieve optimal cooling network configuration. The results show that considering different uncertain parameters can lead to a system with better economy and reliability.
CHINESE JOURNAL OF CHEMICAL ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Lun Yang, Yinliang Xu, Hongbin Sun, Wenchuan Wu
Summary: This paper proposes a new chance-constrained OPF model that satisfies operational constraints with a given probability without assuming specific probability distributions. The joint chance constraint is decomposed into individual chance constraints using an optimized Bonferroni approximation. Different convex approximations are proposed to formulate the model as tractable forms. The proposed convex approximations can also be extended to incorporate structural information and correlation among reserve chance constraints.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Management
Kevin-Martin Aigner, Jan-Patrick Clarner, Frauke Liers, Alexander Martin
Summary: This paper proposes a mathematical optimization model and its solution for joint chance constrained DC Optimal Power Flow. The proposed model minimizes curtailment of renewable energy feed-in while ensuring a high probability of transmission limits being maintained. The solution approach is based on robust safe approximation and replaces probabilistic constraints with suitably defined uncertainty sets constructed from historical data. Experimental results demonstrate the effectiveness and efficiency of this method.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Chemistry, Physical
Gang Wu, Ting Li, Weiting Xu, Yue Xiang, Yunche Su, Jiawei Liu, Fang Liu
Summary: This paper presents an optimal energy-reserve scheduling model for wind-photovoltaic-hydrogen integrated energy systems with multi-type energy storage devices. The model considers the impact of renewable and load uncertainties on reserve constraints using chance-constrained programming theory. An improved discretized step transformation method is proposed to convert the non-convex CCP problem into a solvable mixed integer linear programming formulation. Additionally, a critical threshold value selection approach is developed to reduce constraints and improve solution efficiency. Case studies show that the proposed model reduces operating costs while ensuring system safety. The combined method of improved discretized step transformation and critical threshold value selection reduces computational burden and improves scheduling accuracy.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Environmental Sciences
Yumin Wang, Guangcan Zhu
Summary: In this study, an inexact left-hand side chance-constrained programming (ILCCP) model was proposed and applied to two water distribution systems to address the uncertainty in chlorine decay process and lower and upper chlorine concentration limits. By using Monte Carlo simulation linked with EPANET software, the response coefficients matrix was represented as random variables with normal probability distribution in the constraints of lower and upper limits. The results showed that the lower bounds of optimal injection mass increased with the rise of probability lever for lower limits, while the upper bounds decreased with the rise of the probability level for upper limits, providing valuable insights for managers to determine chlorine injection mass under uncertain scenarios in more complex water distribution systems.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Environmental Sciences
Qianqian Zhang, Zhong Li, Wendy Huang
Summary: The proposed ICCQP-WQM model effectively incorporates uncertainties and provides different cost-effective schemes for seasonal water quality management.
ENVIRONMENTAL RESEARCH
(2021)
Article
Engineering, Electrical & Electronic
Da Huo, Chenghong Gu, David Greenwood, Zhaoyu Wang, Pengfei Zhao, Jianwei Li
Summary: This paper develops chance-constrained optimization methods for planning and operation of energy hub systems under uncertainty, relaxing nonlinear formulations of power and gas flows by convexification methods and modeling the correlation between geographically close wind generators using Gaussian copula. The results demonstrate that combining system integration via energy hubs with chance-constrained operation can reduce operating costs and increase renewable energy yields, benefitting hub system operators and customers with reduced energy infrastructure investment and energy costs.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Engineering, Chemical
Yena Lee, Alba Carrero-Parreno, Sivaraman Ramaswamy, Jose M. Pinto, Lazaros G. Papageorgiou
Summary: An optimization-based framework is proposed for integrated production and distribution planning of industrial gas supply chains in order to minimize overall cost and satisfy customer demands. The framework utilizes a mixed-integer linear programming model and a two-phase hierarchical solution strategy to efficiently solve the optimization problem. The applicability and efficiency of the framework are demonstrated through an industrial-size case study.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2021)
Review
Biochemical Research Methods
Haneen Alosert, James Savery, Jennifer Rheaume, Matthew Cheeks, Richard Turner, Christopher Spencer, Suzanne S. Farid, Stephen Goldrick
Summary: Ensuring data integrity is essential in the biopharmaceutical sector to meet regulatory standards and ensure patient safety. This paper discusses common data integrity violations, links them to regulatory principles, and highlights the role of validated computerized systems in mitigating data integrity risks.
BIOTECHNOLOGY JOURNAL
(2022)
Article
Biochemical Research Methods
E. Amiri Souri, R. Laddach, S. N. Karagiannis, L. G. Papageorgiou, S. Tsoka
Summary: This article presents a DTI prediction pipeline based on graph embedding and gradient boosted tree classification, which efficiently integrates chemical and genomic spaces and achieves competitive results in predicting new DTIs. By applying the model to validated positive and negative interaction data, many credible novel DTIs were predicted, and some predictions were evaluated using molecular docking.
BMC BIOINFORMATICS
(2022)
Article
Engineering, Chemical
Jude O. Ejeh, Songsong Liu, Lazaros G. Papageorgiou
Summary: In this study, a multi-objective evaluation model based on MILP is proposed for assessing the safe layout of multi-floor process plant using Dow's Fire & Explosion Index (F&EI). Two objectives, total monetary cost and financial risk, are considered and solved through constraint method. The application of the model to an ethylene oxide plant demonstrates that layout reconfiguration can greatly reduce the financial risk, and further improvement in safety levels can be achieved through the installation of protection devices.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2022)
Article
Engineering, Chemical
Sheng-Long Jiang, Lazaros G. Papageorgiou, Ian David L. Bogle, Vassilis M. Charitopoulos
Summary: This paper examines the trade-off between design and operational flexibility of a fluid bed dryer in the tablet manufacturing process. The study shows that different drying times have a significant effect on the process flexibility, with the optimal result obtained at 700 seconds. Flexibility is not affected by changes in flow rate, but only by changes in temperature. A black box model was used to demonstrate the methodology in a commercial setting where access to the full model equation set is often limited.
Article
Engineering, Chemical
Georgios L. Bounitsis, Lazaros G. Papageorgiou, Vassilis M. Charitopoulos
Summary: Optimisation under uncertainty is a focal point in Process Systems Engineering research. This study proposes a data-driven Mixed-Integer Linear Programming model for handling large amount of data for uncertain parameters in solving stochastic programming problems. The proposed approach is shown to have advantages in terms of the quality of generated scenario trees compared to state-of-the-art scenario generation methodologies.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2022)
Article
Computer Science, Interdisciplinary Applications
Yena Lee, Jose M. Pinto, Lazaros G. Papageorgiou
Summary: This work focuses on the integrated optimization of production-distribution planning and allocation of transportation resources for industrial gas supply chains. It proposes a MINLP model and reformulates it as a MILFP model. Additionally, a MOO model is presented as an alternative approach. Different solution strategies are adopted for these models, and industry-relevant case studies are used to validate their applicability and performance.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Engineering, Chemical
Yena Lee, Vassilis M. Charitopoulos, Karthik Thyagarajan, Ian Morris, Jose M. Pinto, Lazaros G. Papageorgiou
Summary: This work addresses a production and inventory routing problem in a liquid oxygen supply chain. A two-level hybrid solution approach is proposed using both exact and metaheuristic methods. A real-world case study demonstrates the applicability and effectiveness of the proposed optimization framework.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2022)
Article
Law
Giovanni De Grandis, Irina Brass, Suzanne S. Farid
Summary: This article investigates the regulation of advanced biotherapeutics in the European Union and shows that it presents several defining features of an adaptive regulation regime. However, more attention needs to be paid to the consequences of adaptive regulations and the evaluation of their performance and public value proposition.
REGULATION & GOVERNANCE
(2023)
Article
Biotechnology & Applied Microbiology
Annabel Lyle, Christos Stamatis, Thomas Linke, Martyn Hulley, Albert Schmelzer, Richard Turner, Suzanne S. Farid
Summary: This paper introduces a decisional tool that assesses the feasibility of using non-scalable technologies at high demands for AAV manufacturing. The tool identifies optimal flowsheets that meet both cost and purity targets. The results show that switching to more scalable upstream and downstream processing alternatives is economically advantageous.
BIOTECHNOLOGY AND BIOENGINEERING
(2023)
Article
Oncology
Yongnan Chen, Songsong Liu, Lazaros G. Papageorgiou, Konstantinos Theofilatos, Sophia Tsoka
Summary: In this study, an optimization model was developed to infer pathway activity based on gene expression values, resulting in improved sample classification accuracy. The model was evaluated on cancer molecular subtype classification, robustness to noisy data, and survival prediction. It also allowed for the identification of disease-important genes and pathways.
Article
Biotechnology & Applied Microbiology
Martina Sebastian, Stephen Goldrick, Matthew Cheeks, Richard Turner, Suzanne S. Farid
Summary: The pursuit for higher antibody production in the industry has resulted in increased cell density cultures. However, this has raised concerns about the impact on subsequent product recovery steps. This article introduces a novel approach that uses data from an automated cell counter to accurately predict solids concentration and cell lysis, reducing evaluation time for high-throughput cell culture systems.
BIOTECHNOLOGY AND BIOENGINEERING
(2023)
Article
Biochemistry & Molecular Biology
Yang Yang, Li Xu, Liangdong Sun, Peng Zhang, Suzanne S. Farid
Summary: Machine learning is widely used in cancer diagnosis and prognosis prediction. This study integrates genomic, clinical, and demographic data of lung adenocarcinoma and squamous cell carcinoma patients to develop predictive models for recurrence and survivability using machine learning algorithms. The decision tree models reveal the importance of genomic information, clinical status, and demographics in predicting outcomes.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
