1,721,062 research outputs found
Design space determination of pharmaceutical processes: Effects of control strategies and uncertainty
No full textThe identification of process Design Space (DS) is of high interest in highly regulated industrial sectors, such as pharmaceutical industry, where assurance of manufacturability and product quality is key for process development and decision-making. If the process can be controlled by a set of manipulated variables, the DS can be expanded in comparison to an open-loop scenario, where there are no controls in place. Determining the benefits of control strategies may be challenging, particularly when the available model is complex and computationally expensive – which is typically the case of pharmaceutical manufacturing. In this study, we exploit surrogate-based feasibility analysis to determine whether the process satisfies all process constraints by manipulating the process inputs and reduce the effect of uncertainty. The proposed approach is successfully tested on two simulated pharmaceutical case studies of increasing complexity, i.e., considering (i) a single pharmaceutical unit operation, and (ii) a pharmaceutical manufacturing line comprised of a sequence of connected unit operations. Results demonstrate that different control actions can be effectively exploited to operate the process in a wider range of inputs and mitigate uncertainty
Alternative sustainable routes to methanol production: Techno-economic and environmental assessment
Full text linkThe chemical sector is one of the largest industrial contributors in terms of CO2 direct emissions. Nowadays, methanol is produced almost exclusively from fossil fuel-based methane, thus the need of studying and developing more sustainable alternative routes is increasing importance over the years. Four different routes are considered in addition to the conventional production process of methanol from methane steam reforming: (i) Biogas Reforming-to-methanol, (ii) Biomass Steam Gasification-to-methanol, (iii) CO2 hydrogenation (alkaline water electrolysis coupled with CO2 Direct Air Capture), (iv) CO2 hydrogenation (polymer electrolyte membrane electrolysis coupled with CO2 Direct Air Capture). Based on process simulation results, techno-economic and carbon impact analyses have been performed for each process. The Biogas Reforming-to-methanol process represents the most promising alternative route from an economic point of view. A methanol selling value of 573 €/t after the application of a carbon tax demonstrated its competitiveness against the conventional production. From an emission point of view, the most performing alternatives are represented by the Biomass Steam Gasification-to-methanol with −0.88 t CO2e/t CH3OH, and the CO2 hydrogenation processes with −1.21 and −1.17 t CO2e/t CH3OH case for alkaline water and polymer electrolyte membrane electrolysis, respectively; however, such performance can be attained only if electricity from renewable energy sources is used. Conversely, such technologies are characterized by high methanol production costs (the methanol selling price should be at least 2.1 times higher than the current one)
Economic and environmental optimisation of mixed plastic waste supply chains in Northern Italy comparing incineration and pyrolysis technologies
Full text linkIn 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 M€ 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 M€ per year. Trade-off Pareto optimal solutions were analysed to identify reasonable trade-off configurations between the two objectives
Introducing social acceptance into the design of CCS supply chains: A case study at a European level
Full text linkThe installation of infrastructures for carbon capture, transport, and storage to tackle the problem of CO2 emissions from European power plants and carbon intensive industries, is of strategic importance to reach future greenhouse gases reduction targets. However, the public reaction to the deployment of these technologies is still uncertain, and opposition may result in either cancellations or delays. This article provides quantitative insights into how social acceptance affects the design of a European CO2 infrastructure. A multi-objective mixed integer linear programming model is developed to optimise the design the entire supply chain, by simultaneously addressing the minimisation of the costs to install and operate the infrastructure and the maximisation of its community acceptance. The goal is to provide optimal supply chains in terms of costs, whilst considering the social behaviour of inhabitants towards the installation and operation of either CO2 pipelines or injection wells. Results demonstrate how the methodology may be exploited to assess the response of local communities and identify design strategies aiming at a trade-off between economic objectives and social acceptance. Although the maximisation of social acceptance leads to a +34% increase in costs with respect to the economic optimum, it is shown that an intermediate solution between the two objectives (i.e., economics against acceptance) entails a just slight increase of +8% with respect to the cost of the best economic configuration
Carbon capture and storage from energy and industrial emission sources: A Europe-wide supply chain optimisation
Full text linkPower, steel, cement and refining sectors are currently responsible for the largest shares of carbon dioxide emissions from stationary sources. Carbon capture and storage is envisioned as a key player for decarbonising the power and industry sectors. To achieve a significant penetration of carbon capture and storage technologies, supply chain optimisation has emerged as a crucial research task for designing such complex systems. A Europe-wide carbon capture and storage supply chain is here optimised via a mixed integer linear programming framework. The most significant carbon dioxide emitters (242 power plants, 25 steel mills, 111 cement plants and 59 refineries) are identified on exact geographic coordinates and included as candidates for capture. Capture plants are thoroughly represented in techno-economic terms, considering scale effects and different technological options. Transport and sequestration stages are implemented for either onshore or offshore operation. Different case studies are taken into account to assess carbon capture and storage policies and results determine optimal configurations in terms of costs, scale effects, technology options and network complexity. The minimum CO2 avoidance cost is 52 €/t, which increases by 9% if power plants are excluded from carbon sources. If offshore storage is preferred to onshore, cost raises by about 40%
Optimal design of sustainable supply chains for critical raw materials recycling in renewable energy technologies
Full text linkThe rising demand for critical raw materials (CRMs) driven by renewable energy technologies poses challenges like price volatility, supply chain disruptions, and geopolitical risks. As most CRMs are sourced internationally, their supply is vulnerable; recycling provides a key solution. In this respect, this study develops a multi-period mixed-integer linear programming model under uncertainty to optimise Italy's CRM recycling supply chain. Photovoltaic panels, electric vehicle batteries, and neodymium magnets from wind turbines and electric vehicles are considered under various future scenarios, including net-zero emissions. Projections for 2030 and 2050 indicate a sharp rise in waste availability, requiring significant infrastructure expansion. Costs are expected to double by 2031, driven primarily by battery recycling, and reach 2154 M€ by 2050 under the net-zero scenario. Strategic placement of facilities is essential to balance processing and transport costs and adapt to evolving waste availability trends, ensuring a resilient and efficient supply chain
A Framework for PLS-SIM Integration
No full textA novel algorithm is presented for the design of inferential estimators forprocess monitoring and control. The algorithm aims at integrating Partial Least Squares (PLS) techniques and Subspace Identification Methods (SIM) to exploit the main advantages of both methodologies. In particular, the algorithm will retain the PLS computational robustness in dealing with large sets of correlated inputs and outputs, whilst profiting by the SIM dynamic description of the system being investigated
Identification of complex models of type 2 diabetes from IVGTT data by model-based design of experiments
No full textIntravenous glucose tolerance tests (IVGTTs) are typically used to assess insulin resistance and insulin secretion activities in subjects affected by type 2 diabetes by adopting minimal models. However, the amount of information that can be obtained from IVGTTs for the purpose of model identification is intrinsically related to the dynamics triggered by the intravenous glucose infusion and to the individual specificity. This paper shows how the information content of clinical data from conventional IVGTTs can be handled by model-based design of experiments (MBDoE) techniques when the goal is to estimate the set of parameters of a complex model of type 2 diabetes. MBDoE allows to analyse and improve the information content of IVGTTs by optimising the sample allocation in such a way as to decrease the degree of correlation between critical parameters. © 2013 Elsevier B.V
An optimal experimental design framework for fast kinetic model identification based on artificial neural networks
Full text linkThe development of mathematical models to describe reaction kinetics is crucial in process design, control, and optimisation. However, distinguishing between different candidate kinetic models presents a non-trivial challenge. Recent works on this topic introduced an approach that employs artificial neural networks (ANNs) to identify kinetic models. In this paper, the ANNs-based model identification approach is expanded by introducing an optimal experimental design procedure. The performance of the method is evaluated through a case study
related to the identification of kinetics in a batch reaction system, where different combinations of experimental design variables and noise level on the measurements are compared to assess their impact on kinetic model identification. The proposed experimental design methodology effectively reduces the number of required experiments while enhancing the artificial neural network’s ability to accurately identify the appropriate set of equations defining the kinetic model structure
Optimal Indicator-Variable Approach for Trajectory Synchronization in Uneven-Length Multiphase Batch Processes
Full text linkPartial least-squares regression models assessing the end-point product quality in batch processes require that all of the measured variable trajectories across the historical batches have the same length. Most of the conventional and advanced methodologies for batch synchronization need some prior knowledge about the process to carry out one or more of the following activities: partitioning of the batches into phases, selection of an appropriate indicator variable that is then used to synchronize the batches, or selection of a reference batch to which all other batches are matched. We present an optimal indicator-variable approach for phase partitioning and trajectory synchronization in uneven-length multiphase batch processes. The main advantages are that partitioning into phases and selection of the most appropriate indicator variable within each phase are performed automatically rather than manually and are carried out simultaneously rather than disjointly based on a surrogate optimization framework that maximizes the performance of the product quality assessment model under development. Therefore, differently from conventional and advanced synchronization methodologies currently available, the proposed method is completely process-agnostic, which enhances applicability to complex batch processes. Also, in terms of computational times, it scales favorably with the calibration data set size. An industrial fed-batch process for the manufacturing of a specialty chemical and a simulated fed-batch process for the manufacturing of penicillin are used as test beds and demonstrate that the new indicator-variable approach has a superior performance than models built using other synchronization strategies
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