By Petar Varbanov , Peng Yen Liew Professor Jiri Klemes
The twenty fourth eu Symposium on computing device Aided method Engineering creates a global discussion board the place medical and business contributions of computer-aided recommendations are provided with purposes in strategy modeling and simulation, method synthesis and layout, operation, and strategy optimization.
The organizers have broadened the limits of technique platforms Engineering by means of inviting contributions at diversified scales of modeling and demonstrating vertical and horizontal integration. Contributions variety from functions on the molecular point to the strategic point of the provision chain and sustainable improvement. They disguise significant classical subject matters, even as exploring a brand new diversity of functions that handle the creation of renewable varieties of strength, environmental footprints and sustainable use of assets and water.
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Additional info for 24th European Symposium on Computer Aided Process Engineering. Part A
Considering that we have to replace it every year. The energy and cooling needs are reported in Table 2 per kg of product. The energy needs represent only 10 % compared to biodiesel production (Martín and Grossmann, 2012) so that the integrated plant biodiesel-polyester production does not add up high energy consumption. 28 % of the final product is solid and 72 % is liquid, see Table 3. 46 Bueno et al. Table 1. Requirements of raw materials Raw Material Quantity (t/year) Glycerol 25,600 Adipic Acid 33,378 Dibutylin Dilaurate 94 Table 2.
2. , 2013), is presented in Figure 2, where the computer-aided tools used in the different stages of the framework are highlighted. , 1997), except for process simulators (PROII and ASPEN). The first stage of the framework determines a Achieving More Sustainable Designs through a Process Synthesis-Intensification Framework 33 base case design at the Unit-Ops level considering a superstructure of all possible alternatives based on known technologies. , 2013) to systematically and logically identify process bottlenecks, which sets the targets for process improvement.
In order to maximize the efficiency and avoid excessively sized plant designs, different individual sources of uncertainty are evaluated in this study, moving away from lumped uncertainty safety factors. In this step, the domain of uncertainty is defined with respect to the selected uncertain parameters. By means of sampling the uncertain domain, future scenarios are generated assuming different realizations of uncertain parameters. Latin hypercube sampling (LHS) was used to sample the uncertain space uniformly.