Life cycle assessment (LCA) of electricity generation from rice husk in Malaysia

AbstractThis paper evaluated the life cycle analysis (LCA) of electricity derived from rice husk combustion in the Malaysia rice mills. Due to environment and security constraint cause by fossil fuel, biomass like rice husk becomes an attractive solution to look at. However, the environment profile of the electricity production from rice husk must be assessed to ensure it environment safety. The unit processes that make up the system are the paddy production, transportation to the rice mill, rice mill processing and combustion of rice husk to generate electricity. This study used functional unit as, 1.5MWh of electricity generating at the energy plant. The result show transportation contributes more to climate change compare to other process. Then, the characterized data from rice huskderived electricity is compared with coal and natural gas derived electricity. The results indicate the performance of rice husk derived-electricity is better in the aspect of environment impact parameters

Logistic Cost Analysis of Rice Straw to Optimize Power Plant in Malaysia

Abstract: This paper presents a logistic cost analysis of rice-straw based power generation. Mathematical logistic models were developed to determine collection, storage and transportation costs of rice-straw based power generation. The optimization technique was used to identify the location of power plant and optimum number of storage facilities. The results indicated that transportation costs were the highest of the logistic costs, contributing 54% to 63% of the total logistic costs and that transportation of rice straw to collection centres contributed 89.9% of the total transportation costs due to effect of truck capacity. Reduction in the number of storage facilities would improve transportation cost

Short-term operation of microgrids with thermal and electrical loads under different uncertainties using information gap decision theory

The utilization of an Energy Management System (EMS) for the optimum scheduling of generation units, as well as demand side resources is essential due to the high penetration of Distributed Energy Resources (DERs) in microgrids (MGs), to achieve the desired objectives. As a result of the restructuring of the power systems and increasing the electricity prices during some periods in a day, demand side programs have been highly valuable by electricity customers. In this paper, a Demand Response (DR) model has been proposed to present the behavior of responsive controllable loads in response to the DR calls. Moreover, optimal scheduling of energy resources is developed for a typical MG by considering the presence of both electrical and thermal demands. Combined Heat and Power (CHP) units, boilers, wind turbines, storage devices, demand response resources (DRRs), as well as the power exchange possibility with the upstream wholesale market are the energy resources that have been considered as the portfolio of the decision maker. Furthermore, the uncertainty resources of the wind speeds and electrical load are handled by the Information Gap Decision Theory (IGDT) method. The performance of the proposed framework is comprehensively analyzed on the IEEE 33-bus test system. The advantage of the proposed methodology under the uncertainty conditions is analyzed by the Monte-Carlo simulation method when the different realization of the wind power and electrical load are considered.©2020 Elsevier Ltd. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study.

BACKGROUND: Stereotactic radiotherapy is a standard treatment option for patients with brain metastases. The planning target volume is based on gross tumor volume (GTV) segmentation. The aim of this work is to develop and validate a neural network for automatic GTV segmentation to accelerate clinical daily routine practice and minimize interobserver variability. METHODS: We analyzed MRIs (T1-weighted sequence ± contrast-enhancement, T2-weighted sequence, and FLAIR sequence) from 348 patients with at least one brain metastasis from different cancer primaries treated in six centers. To generate reference segmentations, all GTVs and the FLAIR hyperintense edematous regions were segmented manually. A 3D-U-Net was trained on a cohort of 260 patients from two centers to segment the GTV and the surrounding FLAIR hyperintense region. During training varying degrees of data augmentation were applied. Model validation was performed using an independent international multicenter test cohort (n=88) including four centers. RESULTS: Our proposed U-Net reached a mean overall Dice similarity coefficient (DSC) of 0.92 ± 0.08 and a mean individual metastasis-wise DSC of 0.89 ± 0.11 in the external test cohort for GTV segmentation. Data augmentation improved the segmentation performance significantly. Detection of brain metastases was effective with a mean F1-Score of 0.93 ± 0.16. The model performance was stable independent of the center (p = 0.3). There was no correlation between metastasis volume and DSC (Pearson correlation coefficient 0.07). CONCLUSION: Reliable automated segmentation of brain metastases with neural networks is possible and may support radiotherapy planning by providing more objective GTV definitions