The effect of water containing sodium sulfate ions on strength of concrete of aquaculture ponds and channels

Aquaculture is among the oldest occupations of human being. Over the past quarter of century, the aquaculture industry has grown rapidly. The effect of water containing sodium sulfate on long term compressive strength of concrete of fishing ponds and channels is investigated in this paper. Aim of this paper was to analyze the strength of concrete channels and of aquaculture which are in direct contact with dissolved sodium sulfate. This is an ongoing laboratory investigation which consisted of 480 standard casting concrete cube mix designs and subjecting them to different curing condition environments. Analyzing laboratory results, it was found that for short period of time, the effect was negligible, but for longer periods up to seven months, EC (electrical conductivity) of water had a low negative effect on compressive strength of concrete, while specimens were placed in waters with different ECs. On the other hand, average compressive strength of concrete was almost 25 kg/cm^2 lower than estimated. However, loading the sample concretes up to failure resulted in strength loss of up to 10%. To solve this problem, designed compressive strength must be considered 10% higher than actual in order to have an acceptable concrete strength for water channels and ponds which are in direct contact with sodium sulfate ions in the water

Implementation of Value Engineering for strategy formulation (Case study: Fisheries sector)

This paper analyzes the results of implementing Value Engineering (VE) into SWOT methodology undertaken in Yazd province of Iran. This is the first time that these techniques have been undertaken for fisheries sector in Iran. The study mainly aims at applying Value Engineering and SWOT techniques to achieve the best out of decision-making, and also of management issues as a whole, through the consideration of creative ideas for improvement. The analyses use scientific trawl data and standardized analysis techniques. Based upon the analysis of fisheries issues, findings indicate that the optimized policy is to introduce new sea food chain restaurants into the studied province. Moreover, other suggestions could be as follows: advertising and good standard packaging for export, building facilities for processing and refrigeration by investors, producing Salmon for export in good packaging, producing crabs and other new species, encouraging people to consume more fish, paying great attention to research works, government supporting for transportation, loan, and subsidies, and finally encouraging investors

A Critical Review of Short-Term Water Demand Forecasting Tools—What Method Should I Use?

The challenge for city authorities goes beyond managing growing cities, since as cities develop, their exposure to climate change effects also increases. In this scenario, urban water supply is under unprecedented pressure, and the sustainable management of the water demand, in terms of practices including economic, social, environmental, production, and other fields, is becoming a must for utility managers and policy makers. To help tackle these challenges, this paper presents a well-timed review of predictive methods for short-term water demand. For this purpose, over 100 articles were selected from the articles published in water demand forecasting from 2010 to 2021 and classified upon the methods they use. In principle, the results show that traditional time series methods and artificial neural networks are among the most widely used methods in the literature, used in 25% and 20% of the articles in this review. However, the ultimate goal of the current work goes further, providing a comprehensive guideline for engineers and practitioners on selecting a forecasting method to use among the plethora of available options. The overall document results in an innovative reference tool, ready to support demand-informed decision making for disruptive technologies such as those coming from the Internet of Things and cyber–physical systems, as well as from the use of digital twin models of water infrastructure. On top of this, this paper includes a thorough review of how sustainable management objectives have evolved in a new era of technological developments, transforming data acquisition and treatment.</jats:p

Social sustainability of treatment technologies for bioenergy generation from the municipal solid waste using best worst method

Despite the fundamental role of the social aspect in the implementation of sustainability in the bio-based industries, most of the sustainability assessments research have addressed the environmental and economic dimensions. However, the social dimension has been neglected and it can cause an irreparable outcome in the biotechnology industries. Following this issue, this study propounds a modified systemic approach for a social sustainability impact assessment of the treatment technologies for converting waste into bioenergy, based on a review on the common social assessment methods. As it is known, the guideline presented by the United Nations Environment Program (UNEP) and the Society of Environmental Toxicology and Chemistry (2009) due to considering social life cycle assessment has a comprehensive look at the stakeholders. Therefore, in this paper, UNEP method was selected. However, it needs to be modified based on the bio-energy supply chain derived from municipal solid waste. For this purpose, the bioenergy value chain derived from municipal solid waste was designed and combined with UNEP guideline, to complete the level of stakeholder subgroups and the levels of the indicators. The final method of the social assessment system was presented to the board of experts and finalized. In order to design the measurement part of the social assessment system, because of a multi criteria decision making nature of the social sustainability evaluation of the conversion technologies of municipal solid waste to bio-energies, a recent developed multi-criteria decision making method so-called Best Worst Method (BWM) was used in two stages. The criteria are ranked according to their average weight obtained through Best Worst method. One of the major novelties in this research is the way of application of the best worst technique in the second stage. The model was implemented in the case of Tehran as one of the pioneering Iranian municipalities with high potential to produce bioenergy. The results of this study help decision makers to decide where to concentrate their attention during the implementation stage, and to increase social sustainability in their bioenergy supply chains derived waste

Extreme learning machine for prediction of heat load in district heating systems

District heating systems are important utility systems. If these systems are properly managed, they can ensure economic and environmental friendly provision of heat to connected customers. Potentials for further improvement of district heating systems' operation lie in improvement of present control strategies. One of the options is introduction of model predictive control. Multistep ahead predictive models of consumers' heat load are starting point for creating successful model predictive strategy. In this article, short-term, multistep ahead predictive models of heat load of consumer attached to district heating system were created. Models were developed using the novel method based on Extreme Learning Machine (ELM). Nine different ELM predictive models, for time horizon from 1 to 24 h ahead, were developed. Estimation and prediction results of ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ELM approach in comparison with GP and ANN. Moreover, achieved results indicate that developed ELM models can be used with confidence for further work on formulating novel model predictive strategy in district heating systems. The experimental results show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms