1,720,961 research outputs found
Life cycle assessment of a carbon capture utilization and storage supply chain in Italy and Germany: comparison between carbon dioxide storage and utilization systems
Full text linkThe main purpose of this work is to verify that the CCUS supply chains at large scale that were developed in previous studies for Italy and Germany effectively reduce carbon emissions. The methodology of life cycle analysis was applied. Results showed that the annual global warming potential (GWP) for these supply chains in Italy and Germany are respectively 9.62 × 1010 kgCO2-eq and 1.94 × 1011 kgCO2-eq which would help enable these countries to achieve the carbon dioxide reduction target fixed by European environmental policies. Overall emissions in Italy and Germany are 249 Mtonne/year and 640 Mtonne/year, respectively. Sensitivity analysis results show that, for the supply chain in Germany, the GWP increases when, for a fixed amount of emissions captured, more carbon dioxide is sent to utilization: storage is then important to achieve the environmental target. Other impact categories decrease, increase or remain constant. On the other hand, for the supply chain in Italy, results showed that a lower environmental impact can be obtained by increasing the carbon utilization rate for methane production via a power to gas system. If this is implemented then this utilization system would a better solution from an environmentally point of view than the storage option with other utilization processes
Investigation in to the Application of PLS in MPC Schemes
Full text linkSince its introduction in 1975, Partial Least Squares (PLS) has become a standard tool for developing regression models when data sets contain highly correlated variables. In this paper, the implementation of PLS models within a Model Predictive Control (MPC) scheme is investigated. A significant problem when developing an MPC application is generating suitable data to identify an accurate model of the process. To address issues that can result when the data available for identifying a model is not sufficiently exciting, several researchers have recently suggested using PLS, rather than more traditional identification algorithms when developing predictive models. This paper shows that caution should be exercised when using models identified with PLS in an MPC algorithm. It is shown that if the data available for modelling is not highly correlated then traditional techniques can produce models that provide improved predictive capabilities. However, when limited data is available then there are benefits in using standard PLS. Furthermore, the paper proposes several methods that can be applied to generate unbiased models with varying structures
Investigation in to the Application of PLS in MPC Schemes
Full text linkSince its introduction in 1975, Partial Least Squares (PLS) has become a standard tool for developing regression models when data sets contain highly correlated variables. In this paper, the implementation of PLS models within a Model Predictive Control (MPC) scheme is investigated. A significant problem when developing an MPC application is generating suitable data to identify an accurate model of the process. To address issues that can result when the data available for identifying a model is not sufficiently exciting, several researchers have recently suggested using PLS, rather than more traditional identification algorithms when developing predictive models. This paper shows that caution should be exercised when using models identified with PLS in an MPC algorithm. It is shown that if the data available for modelling is not highly correlated then traditional techniques can produce models that provide improved predictive capabilities. However, when limited data is available then there are benefits in using standard PLS. Furthermore, the paper proposes several methods that can be applied to generate unbiased models with varying structures
Prediction of the Permeability of Packed Beds of Non-Spherical Particles
No full textThis paper details the ongoing development of a virtual permeameter which will have value in the design and performance assessment of filters used in a variety of chemical and process engineering applications. Having previously established the basic simulation requirements for such a permeameter with spherical glass beads, further experimental investigation and associated simulations are reported for non-spherical particles, namely mono-disperse sand and arbitrary poly-disperse, polymorphous minerals. To test the validity and applicability of the previously established guidelines with regard to sample size, resolution and accuracy, the micro-structural details of representative porous media samples are acquired using x-ray micro-tomography. Small sample arrays of such media are then used as input into lattice Boltzmann method (LBM) simulations for predicting their bulk permeability and related properties under laminar flow conditions. It is established that LBM is able to predict the flow rates through the beds at varying fluid pressures, with average error margins between experimental data and simulation predictions of 28% for glass beads, 27% for silica sand and 31% for polymorphous particles
Synthesis and Design of Processing Networks: Stochastic Formulation and Solution
Full text linkIn this contribution, we propose an integrated business and engineering framework for synthesis and design of processing networks under uncertainty. In our framework, an adapted formulation of the transhipment problem is integrated with a superstructure, leading to a Stochastic Mixed Integer Non Linear Program (sMINLP), which is solvedto determine simultaneously the optimal strategic and tactical decisions with respect to the processing network, the material flows, raw material and product portfolio. The framework allows time-effective and robust formulation, solution and analysis of largescale synthesis problems in presence of uncertainty parameters, contributing to broaden the range of application of stochastic programming and optimization to real industrial problems. The framework is applied to an industrial case study based on soybean processing, to identify the optimal processing network under market and technical uncertainty
Energy Management Strategies for Process site CO<sub>2</sub> Emissions Reduction
No full textConcerns about global warming have led governments to regulate CO2 emissions, including through emissions caps, trading and penalties, thus creating economic incentives to reduceCO2 emissions. Two retrofit approaches for CO2 emissions reduction in a process site are introduced, addressing strategies such as heat exchanger network (HEN) retrofit, operational optimization of the utility system and fuel switching. The first approach presents a conceptual method for analysis of the trade-offs between the cost of retrofit and CO2 emissions penalties. The second approach proposes a mathematical model for optimization of emissions reduction strategies. Optimization allows interactions between the HEN and the utility system to be explored systematically using a superstructure of CO2 reduction options. The design methods are applied to a case study and the results are discussed
An Efficient Unit-Specific Event-Based Continuous-Time \{MILP\} Formulation for Short-Term Scheduling of Multistage and Multiproduct Batch Plants
No full textAbstract In this paper, we address the scheduling problem of multistage and multiproduct batch plants involving single product batch per stage. We develop an efficient unit-specific event-based continuous-time formulation in which we allow the empty event points at which no order (task or batch) is processed to start or end after the scheduling horizon or makespan and thus propose new tighter lower bounds for some variables and several tightening constraints to improve the model performance. The computational results show that the proposed formulation is much tighter and superior to that of Castro and Novais, Ind. Eng. Chem. Res. 2008, 47, 6126-6139 and is comparable to the formulation of Liu and Karimi, Comput. Chem. Eng. 2007, 62, 1549-1566
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
