A framework for early-stage sustainability assessment of innovation projects enabled by weighted sum multi-criteria decision analysis in the presence of uncertainty.

A two-level hierarchical framework for early-stage sustainability assessment (FESSA) amongst a set of alternatives applicable from the earliest stages of process or product development is introduced, and its use in combination with an improved method weighted-sum method multi-criteria decision analysis (WSM-MCDA) in the presence of uncertainty is presented through application to a case study based upon a real-world decision scenario from speciality polymer manufacture. The approach taken addresses the challenge faced by those responsible for innovation management in the manufacturing process industries to make simultaneously timely and rational decisions early in the innovation cycle when knowledge gaps and uncertainty about the options tend to be at their highest. The Computed Uncertainty Range Evaluations (CURE) WSM-MCDA method provides better discrimination than the existing Multiple Attribute Range Evaluations (MARE) method without the computational burden of generating heuristic outcome distributions via Monte-Carlo simulation.

This paper introduces a framework that teams can use to think systematically about the wide range of criteria which go into deciding whether a proposed innovation enhances sustainability or not and shows how an improved method for multiple-criteria decision analysis can be used to put it into practice with an example drawn from the speciality chemicals industry. Innovation in the manufacturing process industries requires decisions to be made. In individual projects, scientists and technical managers must decide which technology, materials, and equipment to use. Equally, those responsible for directing a portfolio of projects must choose which projects to prioritise. In either case, early decision making is desirable to avoid sinking time and money into dead-end projects, and to identify what further work is needed for projects with a future. The earlier you decide however, the harder it can be to obtain firm evidence (e.g. conclusive experimental data, fully validated costings, or life cycle impacts) upon which to base your decision. The growing societal expectation that sustainability criteria are factored into such decisions merely adds to the challenges faced by the decision maker. Decisions must be made upon the evidence that is available combined with the informed judgement of those with knowledge of the system under consideration. This is best approached as a facilitated, team-based activity where assertions, assumptions and interpolations or extrapolations from the limited data can be tested and challenged. A sound decision-making process needs a suitable computational method for turning this complex qualitative and semi-quantitative assessment into a clear output indicator of potential success or failure for the options under consideration. The method described in this paper addresses this need but, just as importantly, the methodology ensures that the thought process behind whatever decision is indicated is clearly and transparently documented for future reference.

A novel approach for perfusion process design based on a "Grey-Box" kinetic model.

Perfusion cell-culture mode has caught industrial interest in the field of biomanufacturing in recent years. Thanks to new technology, perfusion-culture processes can support higher cell densities, higher productivities and longer process times. However, due to the inherent operational complexity and high running costs, the development and design of perfusion-culture processes remain challenging. Here, we present a model-based approach to design optimized perfusion cultures of Chinese Hamster Ovary cells. Initially, four batches of bench-top reactor continuous-perfusion-culture data were used to fit the model parameters. Then, we proposed the model-based process design approach, aiming to quickly find out the "theoretically optimal" operational parameters combinations (perfusion rate and the proportion of feed medium in perfusion medium) which could achieve the target steady-state VCD while minimizing both medium cost and perfusion rate during steady state. Meanwhile, we proposed a model-based dynamic operational parameters-adjustment strategy to address the issue of cell-growth inhibition due to the high osmolality of concentrated perfusion medium. In addition, we employed a dynamic feedback control method to aid this strategy in preventing potential nutrient depletion scenarios. Finally, we test the feasibility of the model-based process design approach in both shake flask semi-perfusion culture (targeted at 5 × 107 cells/ml) and bench-top reactor continuous perfusion culture (targeted at 1.1 × 108 cells/ml). This approach significantly reduces the number of experiments needed for process design and development, thereby accelerating the advancement of perfusion-mode cell-culture processes.

Metastable CoCu2O3 Nanocrystals from Combustion-Aerosols for Molecular Sensing and Catalysis.

Metastable nanostructures are kinetically trapped in local energy minima featuring intriguing surface and material properties. To unleash their potential, there is a need for non-equilibrium processes capable of stabilizing a large range of crystal phases outside thermodynamic equilibrium conditions by closely and flexibly controlling atomic reactant composition, spatial temperature distribution and residence time. Here, the capture of metastable pseudo-binary metal oxides at room temperature is demonstrated with scalable combustion-aerosol processes. By a combination of X-ray diffraction, electron microscopy and on-line flame characterization, the occurrence of metastable CoCu2O3 is investigated with controlled crystal size (4-16 nm) over thermodynamically stable CuO and Co3O4. Immediate practical impact is demonstrated by exceptional sensing and stable catalytic performance for air pollutant detection (e.g., 15 parts-per-billion benzene) shown for, at least, 21 days. This approach can be extended to various binary, ternary and high entropy oxides with even more components. Also, secondary phases can be loaded on such metastable nanocrystals to access novel materials promising for actuators, energy storage or solar cells.

Digital design of an integrated purification system for continuous pharmaceutical manufacturing.

In this work dynamic models of the continuous crystallization, filtration, deliquoring, washing, and drying steps are introduced, which are developed in the open-source pharmaceutical modeling tool PharmaPy. These models enable the simulation and digital design of an integrated continuous two-stage crystallization and filtration-drying carousel system. The carousel offers an intensified process that can manufacture products with tailored properties through optimal design and control. Results show that improved crystallization design enhances overall process efficiency by improving critical material attributes of the crystal slurry for downstream filtration and drying operations. The digital design of the integrated process achieves enhanced productivity while satisfying multiple design and product quality constraints. Additionally, the impact of model uncertainty on the optimal operating conditions is investigated. The findings demonstrate the systematic process development potential of PharmaPy, providing improved process understanding, design space identification, and optimized robust operation.

Component innovations for lower cost mechanical vapor compression.

Despite significant capital and operating costs, mechanical vapor compression (MVC) remains the preferred technology for challenging brine concentration applications. This work seeks to assess the dependence of MVC costs on feedwater salinity and desired water recovery and to quantify the value of improved component performance or reduced component costs for reducing the levelized cost of water (LCOW) of MVC. We built a cost optimization model coupling thermophysical, heat and mass transfer, and technoeconomic models to optimize and identify low cost MVC system designs as a function of feedwater salinity and water recovery. The LCOW ranges over 3.6 to 6.1 $/m3 for seawater feed salinities of 25-150 g/kg and water recoveries of 40-80 %. We then perform sensitivity analysis on parameter inputs to isolate irreducible costs and determine high value component innovation targets. The LCOW was most sensitive to evaporator material costs and performance, including the overall heat transfer coefficient in the evaporator. Process and material innovations such as polymer-composite evaporator tubes that reduce evaporator costs by 25 % without reducing heat transfer performance by more than 10 % would result in MVC cost reductions of 8 %.

Modeling of cell cultivation for monoclonal antibody production processes considering lactate metabolic shifts.

Demand for monoclonal antibodies (mAbs) is rapidly increasing. To achieve higher productivity, there have been improvements to cell lines, operating modes, media, and cultivation conditions. Representative mathematical models are needed to narrow down the growing number of process alternatives. Previous studies have proposed mechanistic models to depict cell metabolic shifts (e.g., lactate production to consumption). However, the impacts of variations of some operating conditions have not yet been fully incorporated in such models. This paper offers a new mechanistic model considering variations in dissolved oxygen and glutamine depletion on cell metabolism applied to a novel Chinese hamster ovary (CHO) cell line. Expressions for the specific rates of lactate production, glutamine consumption, and mAb production were formulated for stirred and shaken-tank reactors. A deeper understanding of lactate metabolic shifts was obtained under different combinations of experimental conditions. Lactate consumption was more pronounced in conditions with higher DO and low glutamine concentrations. The model offers mechanistic insights that are useful for designing advanced operation strategies. It can be used in design space generation and process optimization for better productivity and product quality.

Advancements in CHO metabolomics: techniques, current state and evolving methodologies.

Background: Investigating the metabolic behaviour of different cellular phenotypes, i.e., good/bad grower and/or producer, in production culture is important to identify the key metabolite(s)/pathway(s) that regulate cell growth and/or recombinant protein production to improve the overall yield. Currently, LC-MS, GC-MS and NMR are the most used and advanced technologies for investigating the metabolome. Although contributed significantly in the domain, each technique has its own biasness towards specific metabolites or class of metabolites due to various reasons including variability in the concept of working, sample preparation, metabolite-extraction methods, metabolite identification tools, and databases. As a result, the application of appropriate analytical technique(s) is very critical. Purpose and scope: This review provides a state-of-the-art technological insights and overview of metabolic mechanisms involved in regulation of cell growth and/or recombinant protein production for improving yield from CHO cultures. Summary and conclusion: In this review, the advancements in CHO metabolomics over the last 10 years are traced based on a bibliometric analysis of previous publications and discussed. With the technical advancement in the domain of LC-MS, GC-MS and NMR, metabolites of glycolytic and nucleotide biosynthesis pathway (glucose, fructose, pyruvate and phenylalanine, threonine, tryptophan, arginine, valine, asparagine, and serine, etc.) were observed to be upregulated in exponential-phase thereby potentially associated with cell growth regulation, whereas metabolites/intermediates of TCA, oxidative phosphorylation (aspartate, glutamate, succinate, malate, fumarate and citrate), intracellular NAD+/NADH ratio, and glutathione metabolic pathways were observed to be upregulated in stationary-phase and hence potentially associated with increased cell-specific productivity in CHO bioprocess. Moreover, each of technique has its own bias towards metabolite identification, indicating their complementarity, along with a number of critical gaps in the CHO metabolomics pipeline and hence first time discussed here to identify their potential remedies. This knowledge may help in future study designs to improve the metabolomic coverage facilitating identification of the metabolites/pathways which might get missed otherwise and explore the full potential of metabolomics for improving the CHO bioprocess performances.

From biomass-derived fructose to γ-valerolactone: Process design and techno-economic assessment.

This work proposes a process design and techno-economic assessment for the production of γ-valerolactone from lignocellulosic derived fructose at industrial scale, with the aim of exploring its feasibility, identifying potential obstacles, and suggesting improvements in the context of France. First, the conceptual process design is developed, the process modelled and optimized. Second, different potential scenarios for the energy supply to the process are analyzed by means of a set of economic key performance indicators, aimed at highlighting the best potential profitability scenario for the sustainable exploitation of waste biomass in the context analyzed. The lowest Minimum Selling Price for GVL is obtained at 10 kt/y plant fueled by biomass, i.e. 1.89 €/kg, along with the highest end-of-live revenue, i.e. 113 M€. Finally, a sensitivity and uncertainties analysis, based on Monte Carlo simulations, are carried out on the results in order to test their robustness with respect to key input parameters.

A novel algorithm for optimizing hydrocyclone operations in the decontamination of potentially toxic elements in soils.

We present the Three-Parameter Penalized Attributive Analysis for Upgrading (3PPAA-U) method as a tool for selecting the Best Upgrading Condition (BUC) in process engineering. Conventional approaches tend to consider only maximizing recovery (ε) and minimizing yield (γc); in contrast, the proposed 3PPAA-U introduces and seeks to maximize a third parameter, the grade (λ). This multi-parameter approach has not yet been explored in existing literature. In addition to controlling multiple parameters, the method is also superior to others as it includes inverse standard deviation weighting to avoid the distortion of results due to data dispersion. This reduces the possibility of drawing conclusions based on extreme values. Furthermore, the method can be used with a target-to-distance correction to optimize separation for multi-component feeds. To illustrate our method, we present a practical application of 3PPAA-U. Soil contaminated with potentially toxic elements (PTEs) was subject to hydrocycloning under 12 different experimental conditions. Results of these 12 experiments were compared using 3PPAA-U and conventional methods to identify the best upgrading conditions (BUC). Analysis reveals that the 3PPAA-U approach offers a simple and effective criterion for selecting BUC. Furthermore, 3PPAA-U has uses beyond soil remediation. It offers a versatile tool for optimizing operations across various processing and manufacturing environments offering a way to manage factors such as concentration, temperature, pressure, pH, Eh, grain size, and even broader environmental and economic considerations.

An Integrated Process Analysis for Producing Glycerol Carbonate from CO2 and Glycerol.

Glycerol carbonate (GC) is one of the most attractive green chemicals involved in several applications such as polymer synthesis, e. g., the production of polyurethanes and polycarbonates. This relevant chemical can be produced, in a green way, using CO2 (from carbon capture) and glycerol (a byproduct from biodiesel manufacturing). Therefore, in this work, a comprehensive analysis of the GC production process is conducted based on the following synthesis route: urea-dimethyl carbonate-GC using carbon dioxide and glycerol as the main raw materials where the synthesis pathway was efficiently integrated using Aspen Plus. A techno-economic analysis was performed in order to estimate the required capital investment and operating cost for the whole GC process, providing insights on individual capital cost requirements for the urea, dimethyl carbonate, and GC production sections. A total capital cost of $192.1 MM, and a total operating cost of $225.7 MM/y were estimated for the process. The total annualized cost was estimated as $1,558 USD/t of GC produced, competitive with current market price.

Process design and techno-economic analysis of activated carbon derived from anthracite coal.

Activated carbon (AC), renowned for its versatile applications in water treatment, air purification, and industrial processes, is a critical component in environmental remediation and resource recovery strategies. This study encompasses the process modeling of AC production using anthracite coal as a precursor, involving multiple activation stages at different operating conditions, coupled with a detailed techno-economic analysis aimed at assessing the operational feasibility and financial viability of the plant. The economic analysis explores the investigation of economic feasibility by performing a detailed cashflow and sensitivity analysis to identify key parameters influencing the plant's economic performance, including raw material and energy prices, operational and process parameters. Capital and operational costs are meticulously evaluated, encompassing raw material acquisition, labor, energy consumption, and equipment investment. Financial metrics like Net Present Value (NPV), Internal Rate of Return (IRR), and payout period (POP) are employed, and the results show that AC selling price, raw material cost and plant capacity are the most influential parameters determining the plant's feasibility. The minimum AC production cost of 1.28 $/kg is obtained, corresponding to coal flow rate of 14,550 kg/h. These findings provide valuable insights for stakeholders, policymakers, and investors seeking to engage in activated carbon production from anthracite.

Aqueous-phase product treatment and monetization options of wet waste hydrothermal liquefaction: Comprehensive techno-economic and life-cycle GHG emission assessment unveiling research opportunities.

While wet waste hydrothermal liquefaction technology has a high biofuel yield, a significant amount of the carbon and nitrogen in the feedstock reports to the aqueous-phase product. Pretreatment of this stream before sending to a conventional wastewater plant is essential or at the very least, advisable. In this work, techno-economic and life-cycle assessments were conducted for the state-of-technology baseline and four aqueous-phase product treatment and monetization options based on experimental data. These options can cut minimum fuel selling prices by up to 13 % and life-cycle greenhouse gas emissions by up to 39 % compared to the baseline. These findings highlight the substantial influence of aqueous produce treatment strategies on the entire wet waste hydrothermal liquefaction process, demonstrating the potential for optimizing economic viability and environmental impact through further research and development of milder treatment methods and diversified by-product valorization pathways.

Implementation of mDoE-methods to a microcarrier-based expansion processes for mesenchymal stem cells.

The need for advanced therapy medicinal products (ATMPs) has gained increased attention in recent years. In this respect, a well-designed cell expansion process is needed to efficiently manufacture the required number of cells with the desired product quality. This step is challenging due to the biological complexity of the respective primary cell (e.g., mesenchymal stem cells (MSC)) and the usage of microcarrier-based expansion systems. One accelerating approach for process design is model-assisted Design of Experiments (mDoE) combining mathematical process models and statistical tools. In this study, the mDoE workflow was used for the development of an expansion processes with human immortalized mesenchymal stem cells (hMSC-TERT) and the aim of maximizing cell yield assuming only a limited amount of prior knowledge at a very early stage of development. First, suitable microcarriers for expansion in shake flasks were screened and the differentiation of the cells was proven. Second, initial experiments were performed to generate prior knowledge, which was then used to set up the mathematical model and to estimate the model parameters. Finally, the mDoE was used to determine and evaluate the design space to be performed experimentally. Overall, a cell expansion process using microcarriers in a shake flask culture was successfully implemented and a significant increase in cell yield (up to 6,2-fold) was achieved compared to literature.

Machine learning facilitated the conceptual design of an alum dosing system for phosphorus removal in a wastewater treatment plant.

Wastewater treatment plants (WWTPs) face challenges in controlling total phosphorus (TP), given more stringent regulations on TP discharging. In particular, WWTPs that operate at a small scale lack resources for real-time monitoring of effluent quality. This study aimed to develop a conceptual alum dosing system for reducing TP concentration, leveraging machine learning (ML) techniques and data from a full-scale WWTP containing incomplete TP information. The proposed system comprises two ML models in series: an Alert model based on LightGBM with an accuracy of 0.92, and a Dosage model employing a voting algorithm through combining three ML algorithms (LightGBM, SGD, and SVC) with an accuracy of 0.76. The proposed system has demonstrated the potential to ensure that 88.1% of the effluent remains below the TP discharge limit, which outperforms traditional dosing methods and could reduce overdosing from 61.3 to 12.1%. Furthermore, the SHapley Additive exPlanations (SHAP) analysis revealed that incorporating the output features from the previous cycle and utilizing the results of the Alert model as the input features for dosage prediction could be an effective method for data with limited information. The findings of this study have practical applications in improving the efficiency and effectiveness of TP control in small-scale WWTPs, providing a valuable solution for complying with stringent regulations and enhancing environmental sustainability.

Process development and characterization for integrated continuous bioprocesses-Highlights from N-mAb.

The N-mAb case study was produced by the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) to support teaching and learning for both industry and to accelerate adoption of advanced manufacturing process technologies such as integrated continuous bioprocesses (ICB) for mAbs. Similar to the A-mAb case study, N-mAb presents the evolution of an integrated control strategy, from early clinical through process validation and commercial manufacturing with a focus on elements that are unique to integrated continuous bioprocesses. This publication presents a summary of the process design and characterization chapters to allow a greater focus on the unique elements relevant to that phase of development.

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities.

Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sectors to mitigate climate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data-driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. The developed AI methods can be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain.

General Planar Ideal Flow Solutions with No Symmetry Axis.

Bulk ideal flows constitute a wide class of solutions in plasticity theory. Ideal flow solutions concern inverse problems. In particular, the solution determines part of the boundary of a region where it is valid. Bulk planar ideal flows exist in the case of (i) isotropic rigid/plastic material obeying an arbitrary pressure-independent yield criterion and its associated flow rule and (ii) the double sliding and rotation model based on the Mohr-Coulomb yield criterion. In the latter case, the intrinsic spin must vanish. Both models are perfectly plastic, and the complete equation systems are hyperbolic. All available specific solutions for both models describe flows with a symmetry axis. The present paper aims at general solutions for flows with no symmetry axis. The general structure of the solutions consists of two rigid regions connected by a plastic region. The characteristic lines between the plastic and rigid regions must be straight, which partly dictates the general structure of the characteristic nets. The solutions employ Riemann's method in regions where the characteristics of both families are curvilinear. Special solutions that do not have such regions are considered separately. In any case, the solutions are practically analytical. A numerical technique is only necessary to evaluate ordinary integrals. The solutions found determine the tool shapes that produce ideal flows. In addition, the distribution of pressure over the tool's surface is calculated, which is important for predicting the wear of tools.

Design of a Femtosecond Laser Percussion Drilling Process for Ni-Based Superalloys Based on Machine Learning and the Genetic Algorithm.

Femtosecond laser drilling is extensively used to create film-cooling holes in aero-engine turbine blade processing. Investigating and exploring the impact of laser processing parameters on achieving high-quality holes is crucial. The traditional trial-and-error approach, which relies on experiments, is time-consuming and has limited optimization capabilities for drilling holes. To address this issue, this paper proposes a process design method using machine learning and a genetic algorithm. A dataset of percussion drilling using a femtosecond laser was primarily established to train the models. An optimal method for building a prediction model was determined by comparing and analyzing different machine learning algorithms. Subsequently, the Gaussian support vector regression model and genetic algorithm were combined to optimize the taper and material removal rate within and outside the original data ranges. Ultimately, comprehensive optimization of drilling quality and efficiency was achieved relative to the original data. The proposed framework in this study offers a highly efficient and cost-effective solution for optimizing the femtosecond laser percussion drilling process.

Practical Advice on Scientific Design of Freeze-Drying Process: 2023 Update.

OBJECTIVE: The purpose of this paper is to re-visit the design of three steps in the freeze-drying process, namely freezing, primary drying, and secondary drying steps. Specifically, up-to-date recommendations for selecting freeze-drying conditions are provided based on the physical-chemical properties of formulations and engineering considerations. METHODS AND RESULTS: This paper discusses the fundamental factors to consider when selecting freezing, primary drying, and secondary drying conditions, and offers mathematical models for predicting the duration of each segment and product temperature during primary drying. Three simple heat/mass transfer primary drying (PD) models were tested, and their ability to predict product temperature and sublimation time showed good agreement. The PD models were validated based on the experimental data and utilized to tabulate the primary drying conditions for common pharmaceutical formulations, including amorphous and partially crystalline products. Examples of calculated drying cycles, including all steps, for typical amorphous and crystalline formulations are provided. CONCLUSIONS: The authors revisited advice from a seminal paper by Tang and Pikal (Pharm Res. 21(2):191-200, 2004) on selecting freeze-drying process conditions and found that the majority of recommendations are still applicable today. There have been a number of advancements, including methods to promote ice nucleation and computer modeling for all steps of freeze-drying process. The authors created a database for primary drying and provided examples of complete freeze-drying cycles design. The paper may supplement the knowledge of scientists and formulators and serve as a user-friendly tool for quickly estimating the design space.

Validation of model-based design of experiments for continuous wet granulation and drying.

This paper presents an application case of model-based design of experiments for the continuous twin-screw wet granulation and fluid-bed drying sequence. The proposed framework consists of three previously developed models. Here, we are testing the applicability of previously published unit operation models in this specific part of the production line to a new active pharmaceutical ingredient. Firstly, a T-shaped partial least squares regression model predicts d-values of granules after wet granulation with different process settings. Then, a high-resolution full granule size distribution is computed by a hybrid population balance and partial least squares regression model. Lastly, a mechanistic model of fluid-bed drying simulates drying time and energy efficiency, using the outputs of the first two models as a part of the inputs. In the application case, good operating conditions were calculated based on material and formulation properties as well as the developed process models. The framework was validated by comparing the simulation results with three experimental results. Overall, the proposed framework enables a process designer to find appropriate process settings with a less experimental workload. The framework combined with process knowledge reduced 73.2% of material consumption and 72.3% of time, especially in the early process development phase.