We outline the smart manufacturing challenges for formulated products, which are typically multicom- ponent, structured, and multiphase. These challenges predominate in the food, pharmaceuticals, agricul- tural and sp...We outline the smart manufacturing challenges for formulated products, which are typically multicom- ponent, structured, and multiphase. These challenges predominate in the food, pharmaceuticals, agricul- tural and specialty chemicals, energy storage and energetic materials, and consumer goods industries, and are driven by fast-changing customer demand and, in some cases, a tight regulatory framework. This paper discusses progress in smart manufacturing namely, digitalization and the use of large data- sets with predictive models and solution- nding algorithms in these industries. While some progress has been achieved, there is a strong need for more demonstration of model-based tools on realistic prob- lems in order to demonstrate their bene ts and highlight any systemic weaknesses.展开更多
Three transfection reagents, Lipofectamine® 2000, TransIT-PRO® and linear 25 kDa polyethylenimine were evaluated for transient expression of enhanced green fluorescent protein in Chinese hamster ovary ce...Three transfection reagents, Lipofectamine® 2000, TransIT-PRO® and linear 25 kDa polyethylenimine were evaluated for transient expression of enhanced green fluorescent protein in Chinese hamster ovary cells. TransIT-PRO® was found to be more efficient under the examined conditions, but comes at an increased cost compared to the widely used PEI.展开更多
The Bioprocessing industry delivers high-value protein-based pharmaceutical products produced using microbial or animal cells. Animal cell culture, the only method currently available for the production of proteins wi...The Bioprocessing industry delivers high-value protein-based pharmaceutical products produced using microbial or animal cells. Animal cell culture, the only method currently available for the production of proteins with human-like post-translational modifications, is an expensive and labor-intensive process, as animal cells have complex nutrient requirements. Optimization studies have typically been limited to experimental studies, although there has recently been increased interest in combined experimental and computational approaches. In this work, we present the results of a dynamic optimization approach to improving animal cell bioprocesses. We have based this on a model validated over batch and fed-batch conditions and have examined four possible objective functions. Our results indicate that the maximization of the product concentration or the integral of viable cell concentration over time give equivalent results and can improve the product titer up to 70% over non-optimized fed-batch cultures.展开更多
Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we pr...Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.展开更多
This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns...This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational ow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, ow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody puri cation process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.展开更多
CO_(2)capture,utilization and storage(CCUS)is recognized as a uniquely important option in global efforts to control anthropogenic greenhouse-gas(GHG)emissions.Despite significant progress globally in advancing the ma...CO_(2)capture,utilization and storage(CCUS)is recognized as a uniquely important option in global efforts to control anthropogenic greenhouse-gas(GHG)emissions.Despite significant progress globally in advancing the maturity of the various component technologies and their assembly into full-chain demonstrations,a gap remains on the path to widespread deployment in many countries.In this paper,we focus on the importance of business models adapted to the unique technical features and sociopolitical drivers in different regions as a necessary component of commercial scale-up and how lessons might be shared across borders.We identify three archetypes for CCUS development-resource recovery,green growth and low-carbon grids-each with different near-term issues that,if addressed,will enhance the prospect of successful commercial deployment.These archetypes provide a framing mechanism that can help to translate experience in one region or context to other locations by clarifying the most important technical issues and policy requirements.Going forward,the archetype framework also provides guidance on how different regions can converge on the most effective use of CCUS as part of global deep-decarbonization efforts over the long term.展开更多
In order to achieve holistic urban plans incorporating transport infrastructure,public space and the behavior of people in these spaces,integration of urban design and computer modeling is a promising way to provide b...In order to achieve holistic urban plans incorporating transport infrastructure,public space and the behavior of people in these spaces,integration of urban design and computer modeling is a promising way to provide both qualitative and quantitative support to decisionmakers.This paper describes a systematic literature review following a four-part framework.Firstly,to understand the relationship of elements of transport,spaces,and humans,w e review policy and urban design strategies for promoting positive interactions.Secondly,we present an overview of the integration methods and strategies used in urban design and policy discourses.Afterward,metrics and approaches for evaluating the effectiveness of integrated plan alternatives are reviewed.Finally,this paper gives a review of state-of-the-art tools with a focus on seven com puter simulation paradigms.This article explores mechanisms underlying the complex system of transport,spaces,and humans from a multidisciplinary perspective to provide an integrated toolkit for designers,planners,modelers and decision-m akers with the current methods and their challenges.展开更多
文摘We outline the smart manufacturing challenges for formulated products, which are typically multicom- ponent, structured, and multiphase. These challenges predominate in the food, pharmaceuticals, agricul- tural and specialty chemicals, energy storage and energetic materials, and consumer goods industries, and are driven by fast-changing customer demand and, in some cases, a tight regulatory framework. This paper discusses progress in smart manufacturing namely, digitalization and the use of large data- sets with predictive models and solution- nding algorithms in these industries. While some progress has been achieved, there is a strong need for more demonstration of model-based tools on realistic prob- lems in order to demonstrate their bene ts and highlight any systemic weaknesses.
文摘Three transfection reagents, Lipofectamine® 2000, TransIT-PRO® and linear 25 kDa polyethylenimine were evaluated for transient expression of enhanced green fluorescent protein in Chinese hamster ovary cells. TransIT-PRO® was found to be more efficient under the examined conditions, but comes at an increased cost compared to the widely used PEI.
文摘The Bioprocessing industry delivers high-value protein-based pharmaceutical products produced using microbial or animal cells. Animal cell culture, the only method currently available for the production of proteins with human-like post-translational modifications, is an expensive and labor-intensive process, as animal cells have complex nutrient requirements. Optimization studies have typically been limited to experimental studies, although there has recently been increased interest in combined experimental and computational approaches. In this work, we present the results of a dynamic optimization approach to improving animal cell bioprocesses. We have based this on a model validated over batch and fed-batch conditions and have examined four possible objective functions. Our results indicate that the maximization of the product concentration or the integral of viable cell concentration over time give equivalent results and can improve the product titer up to 70% over non-optimized fed-batch cultures.
文摘Developing a well-predictive machine learning model that also offers improved interpretability is a key challenge to widen the application of artificial intelligence in various application domains. In this work, we present a Data Information integrated Neural Network (DINN) algorithm that incorporates the correlation information present in the dataset for the model development. The predictive performance of DINN is also compared with a standard artificial neural network (ANN) model. The DINN algorithm is applied on two case studies of energy systems namely energy efficiency cooling (ENC) & energy efficiency heating (ENH) of the buildings, and power generation from a 365 MW capacity industrial gas turbine. For ENC, DINN presents lower mean RMSE for testing datasets (RMSE_test = 1.23 %) in comparison with the ANN model (RMSE_test = 1.41 %). Similarly, DINN models have presented better predictive performance to model the output variables of the two case studies. The input perturbation analysis following the Gaussian distribution for noise generation reveals the order of significance of the variables, as made by DINN, can be better explained by the domain knowledge of the power generation operation of the gas turbine. This research work demonstrates the potential advantage to integrate the information present in the data for the well-predictive model development complemented with improved interpretation performance thereby opening avenues for industry-wide inclusion and other potential applications of machine learning.
文摘This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational ow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, ow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody puri cation process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.
文摘CO_(2)capture,utilization and storage(CCUS)is recognized as a uniquely important option in global efforts to control anthropogenic greenhouse-gas(GHG)emissions.Despite significant progress globally in advancing the maturity of the various component technologies and their assembly into full-chain demonstrations,a gap remains on the path to widespread deployment in many countries.In this paper,we focus on the importance of business models adapted to the unique technical features and sociopolitical drivers in different regions as a necessary component of commercial scale-up and how lessons might be shared across borders.We identify three archetypes for CCUS development-resource recovery,green growth and low-carbon grids-each with different near-term issues that,if addressed,will enhance the prospect of successful commercial deployment.These archetypes provide a framing mechanism that can help to translate experience in one region or context to other locations by clarifying the most important technical issues and policy requirements.Going forward,the archetype framework also provides guidance on how different regions can converge on the most effective use of CCUS as part of global deep-decarbonization efforts over the long term.
文摘In order to achieve holistic urban plans incorporating transport infrastructure,public space and the behavior of people in these spaces,integration of urban design and computer modeling is a promising way to provide both qualitative and quantitative support to decisionmakers.This paper describes a systematic literature review following a four-part framework.Firstly,to understand the relationship of elements of transport,spaces,and humans,w e review policy and urban design strategies for promoting positive interactions.Secondly,we present an overview of the integration methods and strategies used in urban design and policy discourses.Afterward,metrics and approaches for evaluating the effectiveness of integrated plan alternatives are reviewed.Finally,this paper gives a review of state-of-the-art tools with a focus on seven com puter simulation paradigms.This article explores mechanisms underlying the complex system of transport,spaces,and humans from a multidisciplinary perspective to provide an integrated toolkit for designers,planners,modelers and decision-m akers with the current methods and their challenges.
