Evaluating the energy use, economic and environmental sustainability for smoked fish production from life cycle assessment point of view (case study: Guilan Province, Iran).

The purpose of this study is to determine energy use patterns, evaluating the environmental impacts and economic evaluation of smoked fish production in the Guilan Province. The initial data were collected from seven smokehouses in the north of Iran through face-to-face questionnaire method, while the required data related to the background system were extracted from the Ecoinvent 2.2 database. The total input and output energy result showed the use is examined to be 98,143.29 and 60,048 MJ ton-1, respectively. About 79.31% of this was generated by fish, 7.7% from electricity, and 6.7% from wood chip. Life cycle assessment results showed that fish and salt emissions have the most notable effects. Among damage categories, the largest emissions were related to human health (37.77%). Climate change (30.35%), resource (28.78%), and ecosystem quality (0.92%) are next phases. The rates of nonrenewable, fossil in CExD method calculated 35,426.61 MJ ton-1. Economic analysis of production was carried out. Some economic indicators have been calculated, and the benefit to cost ratio was obtained as 3.09. Due to the justification of fisheries management, these issues merit further exploration.

Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production.

This study aims to employ two artificial intelligence (AI) methods, namely, artificial neural networks (ANNs) and adaptive neuro fuzzy inference system (ANFIS) model, for predicting life cycle environmental impacts and output energy of sugarcane production in planted or ratoon farms. The study is performed in Imam Khomeini Sugarcane Agro-Industrial Company (IKSAIC) in Khuzestan province of Iran. Based on the cradle to grave approach, life cycle assessment (LCA) is employed to evaluate environmental impacts and study environmental impact categories of sugarcane production. Results of this study show that the consumed and output energies of sugarcane production are in average 172,856.14 MJ ha-1, 120,000 MJ ha-1 in planted farms and 122,801.15 MJ ha-1, 98,850 MJ ha-1 in ratoon farms, respectively. Results show that, in sugarcane production, electricity, machinery, biocides and sugarcane stem cuttings have the largest impact on the indices in planted farms. However, in ratoon farms, electricity, machinery, biocides and nitrogen fertilizers have the largest share in increasing the indices. ANN model with 9-10-5-11 and 7-9-6-11 structures are the best topologies for predicting environmental impacts and output energy of sugarcane production in planted and ratoon farms, respectively. Results from ANN models indicated that the coefficient of determination (R2) varies from 0.923 to 0.986 in planted farms and 0.942 to 0.982 in ratoon farms in training stage for environmental impacts and outpt energy. Results from ANFIS model, which is developed based on a hybrid learning algorithm, showed that, for prediction of environmental impacts, R2 varies from 0.912 to 0.978 and 0.986 to 0.999 in plant and ratoon farms, respectively, and for prediction of output energy, R2 varies from 0.944 and 0.996 in planted and ratoon farms. Results indicate that ANFIS model is a useful tool for prediction of environmental impacts and output energy of sugarcane production in planted and ratoon farms.