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Renewable energy resources have received increasing attention due to environmental concerns. Biomass, one of the most important renewable energy resources, is abundant in agricultural-based countries. Typically, the biomass supply chain is large due to the huge amount of relevant data required for building the model. As a result, using a standard optimization package to determine the solution for the biomass supply chain model might not be practical. In this study, the focus is on developing and applying advanced methodologies that can be used to determine a solution for the biomass supply chain model efficiently. The decisions related to plant selection, and distribution of biomass from suppliers to plants require optimization. The methodologies considered in this research are based on stochastic programming, parameter search, and simulation-based optimization. Computational results and managerial insights based on case studies from different regions of Vietnam are provided. The results show that parameter search is suitable for small problems only, while stochastic programming is suitable for small and medium problems. For large problem, simulation-based optimization performs better when considering the quality of the solution and the run time, although, this method does not guarantee an optimal solution. It provides good solutions where the gaps to the optimal solutions are between 0.59% and 8.41%.

These days, enterprise managers involved with decision-making processes struggle with several problems related to market position or business reputation of their companies. Collecting data and retrieving high-quality set of information is one of the main priority tasks of enterprise managers involved in decision-making processes. To overcome the difficulties that may arise from market competitiveness and gain some kind of competitive advantage, it is important that these managers make the most of adequate tools in order to get the right set of highly refined information. What-If analysis can help managers getting the competitive advantage they need. It allows for simulating hypothetical scenarios and analyzing the consequences of specific changes without harming business activities. In this paper, we propose a hybridization methodology, which combines What-If analysis process with OLAP usage preferences, for optimizing decision processes. We present and discuss the integration of OLAP usage preferences in the conventional What-If analysis with a case example., This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.

The SCALE6.0 simulation model of a typical PWR primary loop components for effective dose rates calculation based on hybrid deterministic–stochastic methodology was created. The criticality sequence CSAS6/KENO-VI of the SCALE6.0 code package, which includes KENO-VI Monte Carlo code, was used for criticality calculations, while neutron and gamma dose rates distributions were determined by MAVRIC/Monaco shielding sequence. A detailed model of a combinatorial geometry, materials and characteristics of a generic two loop PWR facility is based on best available input data. The sources of ionizing radiation in PWR primary loop components included neutrons and photons originating from critical core and photons from activated coolant in two primary loops. Detailed calculations of the reactor pressure vessel and the upper reactor head have been performed. The efficiency of particle transport for obtaining global Monte Carlo dose rates was further examined and quantified with a flexible adjoint source positioning in phase-space. It was demonstrated that generation of an accurate importance map (VR parameters) is a paramount step which enabled obtaining Monaco dose rates with fairly uniform uncertainties. Computer memory consumption by the SN part of hybrid methodology represents main obstacle when using meshes with large number of cells together with high SN/PN parameters. Detailed voxelization (homogenization) process in Denovo together with high SN/PN parameters is essential for precise VR parameters generation which will result in optimized MC distributions. Shielding calculations were also performed for the reduced PWR facility model which included only reactor core and adjacent concrete structures with the steam generator and the pump. Hybrid FW-CADIS methodology appears to be very effective for shielding calculations of complicated MC geometries. It was demonstrated that definition of air as a global adjoint source gave lower uncertainty of photon dose rates. Activation of the primary loop coolant, which becomes additional gamma source in operating reactor, was considered. Compared with analog MC simulations, the hybrid methodology drastically improved Monaco distributions in quality as well in space coverage. The SCALE6.0 hybrid stochastic methodology was thoroughly investigated and its versatile ability for deep penetration models was proved. A careful selection of parameters can relax high CPU time and hardware requirements.

The trend of global energy demand forecasts a continuous increase in the forthcoming years together with a diversification and differentiation of energy sources. In order to ensure the energy systems transition towards a sustainable functioning mode while avoiding early depletion of energy resources, industrial activities need to foster process improvement. Among energy efficient process design methodologies, pinch analysis and exergy analysis are two powerful methods but each one shows drawbacks. This paper introduces a new methodology that uses the Jacobian Matrix of exergy destruction as a mathematical indicator to couple the Pinch and the exergy analysis and overcome their individual limitations. In the proposed approach, exergy analysis is used not only to assess the exergy losses but also to guide the changes needed in industrial processes structures and operating conditions. And while the pinch analysis considers only heat integration, the proposed methodology allows including other recoverable exergy in a process through thermodynamic conversion. In this paper, the guidelines of the methodology are first detailed. When applied on a basic natural gas liquefaction process, the proposed approach will numerically result in a new improved process comparable to the existing, most widely used natural gas liquefaction process. The main advantage of the methodology is time saving compared to holistic operating conditions improvement as in the traditional process design methods and automatic process structure modification leading to better heat integration.

The capabilities of the SCALE6.1/MAVRIC hybrid shielding methodology (CADIS and FW-CADIS) were demonstrated when applied to a realistic deep penetration Monte Carlo (MC) shielding problem of a full-scale PWR containment model. Automatic preparation of variance reduction (VR) parameters is based on deterministic transport theory (SNmethod) providing the space-energy importance function. The aim of this paper was to determine the neutron-gamma dose rate distributions over large portions of PWR containment with uniformly small MC uncertainties. The sources of ionizing radiation included fission neutrons and photons from the reactor and photons from the activated primary coolant. We investigated benefits and differences of FW-CADIS over CADIS methodology for the objective of the uniform MC particle density in the desired tally regions. Memory intense deterministic module was used with broad group library “v7_27n19g” opposed to the fine group library “v7_200n47g” used for final MC simulation. Compared with CADIS and with the analog MC, FW-CADIS drastically improved MC dose rate distributions. Modern shielding problems with large spatial domains require not only extensive computational resources but also understanding of the underlying physics and numerical interdependence betweenSN-MC modules. The results of the dose rates throughout the containment are presented and discussed for different volumetric adjoint sources.