Full-scale demonstration of biological nutrient removal in a single tank SBR process

Complete biological nutrient removal (BNR) in a single tank, sequencing batch reactor (SBR) process, is demonstrated here at full-scale on a typical domestic wastewater. The unique feature of the UniFed process is the introduction of the influent into the settled sludge blanket during the settling and decant periods of the SBR operation. This achieves suitable conditions for denitrification and anaerobic phosphate release which is critical to successful biological phosphorus removal, It also achieves a selector effect, which helps in generating a compact, well settling biomass in the reactor. The results of this demonstration show that it is possible to achieve well over 90% removal of GOD, nitrogen and phosphorus in such a process. Effluent quality achieved over a six-month operating period directly after commissioning was: 29 mg/l GOD, 0.5 mg/l NH4-N, 1.5 mg/l NOx-N and 1.5 mg/l PO4-P (50%-iles of daily samples). During an 8-day, intensive sampling period, the effluent BOD5 was

Find It - An Assistant Home Agent

Cognitive impaired population face with innumerable problems in their daily life. Surprisingly, they are not provided with any help to perform those tasks for which they have difficulties. As a consequence, it is necessary to develop systems that allow those people to live independently and autonomously. Living in a technological era, people could take advantage of the available technology, being provided with some solutions to their needs. This paper presents a platform that assists users with remembering where their possessions are. Mainly, an object recognition process together with an intelligent scheduling applications are integrated in an Ambient Assisted Living (AAL) environment.

Growth and nutrient uptake of coffee seedlings cultivated in nutrient solution with and without silicon addition

In recent years, the application of silicon (Si) in crops, including coffee, has become a common practice. The objective of this study was to assess the silicon uptake by coffee seedlings and its effects on plant growth, water and macro and micronutrient uptake. The research was conducted using nutrient solution in a greenhouse at the Departamento de Fitotecnia da Universidade Federal de Viçosa, in a completely randomized design with two treatments (with and without silicon) and three replications. Each plot consisted of three plants grown in a 800 mL vessel containing the treatment solutions. At every three days, water consumption, the concentration of OH - and the depletion of Si and K were assessed in the nutrient solutions. After 33 days, the plants were assessed with regard to their fresh and dry weight of leaves, roots and stem, shoot height and total length of the plant (shoot and root). Number of leaves and internodes, and the content and accumulation of silicon, macro, and micronutrients were also determined. The consumption of water, the amount of potassium uptake and, biomass accumulation were greater in plants grown in solution without silicon addition. However, the concentration of OH- in the solution and the amount of silicon uptake were greater in plants grown in solution with added silicon. Silicon accumulation was greater in leaves than in stem and roots. Silicon decreased coffee plant accumulation of phosphorus, potassium, calcium, zinc, copper and iron.

Nutrient accumulation and biomass production of alfafa after soil amendment with silicates

Studies on the use of silicate correctives in agriculture show that they have great potential to improve soil chemical characteristics, however, little information is available on the reactivity rates of their particle-size fractions. This study investigated whether the reactivity rates obtained experimentally could be considered in the calculation of ECC (effective calcium carbonate) for soil liming, promoting adequate development of alfalfa plants. Six treatments were evaluated in the experiment, consisting of two slag types applied in two rates. The experimental ECC was used to calculate one of the rates and the ECC determined in the laboratory was used to calculate the other. Rates of limestone and wollastonite were based on the ECC determined in laboratory. The rates of each soil acidity corretive were calculated to increase the base saturation to 80%. The treatments were applied to a Rhodic Hapludox and an Alfisol Ferrudalfs. The methods for ECC determination established for lime can be applied to steel slag. The application of slag corrected soil acidity with consequent accumulation of Ca, P, and Si in alfalfa, favoring DM production.

Phytomass production and nutrient accumulation by green manure species

Green manuring is recognized as a viable alternative to improve nutrient cycling in soils. The aim of this study was to evaluate the phytomass production and nutrient accumulation in shoots of the summer green manures jack bean [Canavalia ensiformis (L.) DC.], dwarf pigeon pea (Cajanus cajanvar var. Flavus DC.), dwarf mucuna [Mucuna deeringiana (Bort) Merr] and sunn hemp (Crotalaria juncea L.), under nitrogen fertilization and/or inoculation with N-fixing bacteria. A split plot design was arranged with the four Fabaceae species as main plots and nitrogen fertilization (with and without) and inoculation with diazotrophic bacteria (with and without) as the subplots, in a 2² factorial. The experiment was arranged as a randomized complete block design with four replications. In the conditions of this trial, the sunn hemp had the highest production of shoot phytomass (12.4 Mg ha-1) and nutrient accumulation, while the dwarf mucuna had the lowest production of shoot phytomass (3.9 Mg ha-1) and nutrient accumulation. The results showed no effect of nitrogen fertilization or inoculation with N-fixing bacteria on the production of shoot phytomass and nutrient accumulation, except for inoculation without nitrogen fertilization, resulting in greater P accumulation (p <0.05) in the sunn hemp and greater Zn and Mn accumulation in the dwarf mucuna. These findings indicate that N fertilization or inoculation with N2-fixing bacteria for Fabaceae are low efficiency practices in the edaphoclimatic conditions of this study.

Growth and nutrient accumulation in mycorrhized papaya seedlings cultivated in a phosphorus-fertilized substrate

ABSTRACT The indiscriminate use of mineral fertilizers in papaya orchards has increased production costs, and the use of arbuscular mycorrhizal fungi is a promising alternative to reduce such expenses. Therefore, the present research aimed at studying the efficiency of arbuscular mycorrhizal fungi (AMF) on dry matter and nutrient accumulation in Sunrise Solo papaya seedlings, by applying doses of P2O5 (triple superphosphate) that are harmful to the symbiosis. The experiment was carried out in a protected environment and was set up in a randomized block design with four replications, and consisted of four P2O5 doses (0, 672, 1386 and 2100 mg dm-3), three mycorrhizal fungi species (Gigaspora margarita, Entrophospora colombiana and Scutellospora heterogama) and the control treatment (mycorrhiza-free). Shoot and root dry matter as well as nitrogen, phosphorus and potassium contents in leaf and root tissues were assessed. Mycorrhizal inoculation promoted a 30% increase in shoot dry matter in relation to the control treatment. Mycorrhizal fungi promoted increases in leaf and root nitrogen content up to 672 mg dm-3 P2O5. Inoculation of E. colombiana favored the highest gains in root and shoot dry matter. P2O5 fertilization increased foliar and root phosphorus content.

Agent Inferencing Meets the Semantic Web

We provide all agent; the capability to infer the relations (assertions) entailed by the rules that, describe the formal semantics of art RDFS knowledge-base. The proposed inferencing process formulates each semantic restriction as a rule implemented within a, SPARQL query statement. The process expands the original RDF graph into a fuller graph that. explicitly captures the rule's described semantics. The approach is currently being explored in order to support descriptions that follow the generic Semantic Web Rule Language. An experiment, using the Fire-Brigade domain, a small-scale knowledge-base, is adopted to illustrate the agent modeling method and the inferencing process.

A hybrid approach to multi-agent decision-making

In the aftermath of a large-scale disaster, agents' decisions derive from self-interested (e.g. survival), common-good (e.g. victims' rescue) and teamwork (e.g. fire extinction) motivations. However, current decision-theoretic models are either purely individual or purely collective and find it difficult to deal with motivational attitudes; on the other hand, mental-state based models find it difficult to deal with uncertainty. We propose a hybrid, CvI-JI, approach that combines: i) collective 'versus' individual (CvI) decisions, founded on the Markov decision process (MDP) quantitative evaluation of joint-actions, and ii)joint-intentions (JI) formulation of teamwork, founded on the belief-desire-intention (BDI) architecture of general mental-state based reasoning. The CvI-JI evaluation explores the performance's improvement

RFID together with multi-agent systems to control global value chains

Nowadays, the cooperative intelligent transport systems are part of a largest system. Transportations are modal operations integrated in logistics and, logistics is the main process of the supply chain management. The supply chain strategic management as a simultaneous local and global value chain is a collaborative/cooperative organization of stakeholders, many times in co-opetition, to perform a service to the customers respecting the time, place, price and quality levels. The transportation, like other logistics operations must add value, which is achieved in this case through compression lead times and order fulfillments. The complex supplier's network and the distribution channels must be efficient and the integral visibility (monitoring and tracing) of supply chain is a significant source of competitive advantage. Nowadays, the competition is not discussed between companies but among supply chains. This paper aims to evidence the current and emerging manufacturing and logistics system challenges as a new field of opportunities for the automation and control systems research community. Furthermore, the paper forecasts the use of radio frequency identification (RFID) technologies integrated into an information and communication technologies (ICT) framework based on distributed artificial intelligence (DAI) supported by a multi-agent system (MAS), as the most value advantage of supply chain management (SCM) in a cooperative intelligent logistics systems. Logistical platforms (production or distribution) as nodes of added value of supplying and distribution networks are proposed as critical points of the visibility of the inventory, where these technological needs are more evident.

DITRANS - a multi-agent system for integrated diagnosis of power transformers

This paper describes the development and the implementation of a multi-agent system for integrated diagnosis of power transformers. The system is divided in layers which contain a number of agents performing different functions. The social ability and cooperation between the agents lead to the final diagnosis and to other relevant conclusions through integrating various monitoring technologies, diagnostic methods and data sources, such as the dissolved gas analysis.

MASCEM improvement for agent based coalitions

This paper presents MASCEM - Multi-Agent Simulator for Electricity Markets improvement towards an enlarged model for Seller Agents coalitions. The simulator has been improved, both regarding its user interface and internal structure. The OOA, used as development platform, version was updated and the multi-agent model was adjusted for implementing and testing several negotiations regarding Seller agents’ coalitions. Seller coalitions are a very important subject regarding the increased relevance of Distributed Generation under liberalised electricity markets.

MASGriP – a Multi-Agent Smart Grid Simulation Platform

The increasing number of players that operate in power systems leads to a more complex management. In this paper a new multi-agent platform is proposed, which simulates the real operation of power system players. MASGriP – A Multi-Agent Smart Grid Simulation Platform is presented. Several consumer and producer agents are implemented and simulated, considering real characteristics and different goals and actuation strategies. Aggregator entities, such as Virtual Power Players and Curtailment Service Providers are also included. The integration of MASGriP agents in MASCEM (Multi-Agent System for Competitive Electricity Markets) simulator allows the simulation of technical and economical activities of several players. An energy resources management architecture used in microgrids is also explained.

A multi-agent based approach for intelligent smart grid management

The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players’ behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM – Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP – Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles’ batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

Multi-agent simulation of continental, regional, and micro electricity markets

Renewable based power generation has significantly increased over the last years. However, this process has evolved separately from electricity markets, leading to an inadequacy of the present market models to cope with huge quantities of renewable energy resources, and to take full advantage of the presently existing and the increasing envisaged renewable based and distributed energy resources. This paper proposes the modelling of electricity markets at several levels (continental, regional and micro), taking into account the specific characteristics of the players and resources involved in each level and ensuring that the proposed models accommodate adequate business models able to support the contribution of all the resources in the system, from the largest to the smaller ones. The proposed market models are integrated in MASCEM (Multi- Agent Simulator of Competitive Electricity Markets), using the multi agent approach advantages for overcoming the current inadequacy and significant limitations of the presently existing electricity market simulators to deal with the complex electricity market models that must be adopted.

Agent-based simulation of electronic marketplaces with decision support

This paper presents a Multi-Agent Market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agents reactions.