Energy and sustainability of operation of a wastewater treatment plant

The study summarises the energy consumption data obtained from the Slovak wastewater treatment plants. Overall, 51 large WWTPs using mesophilic anaerobic sludge digestion and biogas utilisation (the total capacity of 2.5 mil. p.e.) and 17 small rural WWTPs (the total capacity 15 000 p.e.) were compared in many technological and energy parameters. The average energy consumption in large WWTPs in Slovakia is 0.485 kWh/m3 and 0.915 kWh/m3 in small rural plants. The average energy demand related to BOD5 load represents the value of 2.27 kWh/kg BOD5, in Slovak plants. The specific energy production is relatively low - in average 1.2 kWh el /m3 of produced biogas and 0.1 kWh el /m 3 of treated wastewater, respectively. The average energy autarky in Slovak plants is 25.2%. Some plants have high energy autarky (>65%), despite no external biowastes being dosed to these during operation

Biogas Production in Municipal Wastewater Treatment Plants – Current Status in EU with a Focus on the Slovak Republic

The presented contribution reviews actual status of biogas production in the European countries with a focus on the Slovak municipal WWTPs. In 49 monitored Slovak WWTPs (out of 520) the anaerobic digestion with biogas production is operated. The total volume of digestion tanks is about 195 000 m3 but the total daily biogas production is only approx. 55 000 m3 d–1. From a technological point of view, the digestion tanks have sufficient space for considerable increase of biogas production. The increase can be achieved by the choice and dosing of external organic sources that could bring significant energy – economic contribution to WWTP operation without technological process adaptation (plant oils, fats, organic materials, etc.) or with a small technological process adaptation (food residues, food and agricultural products and wastes). The contribution describes the actual load parameters of digestion tanks, specific biogas production, electrical power capacity, and production on the Slovak WWTP obtained on the basis of a questionnaire from Slovak Water Companies

Automatic Exploration of Datacenter performance Regimes

Horizontally scalable Internet services present an opportunity to use automatic resource allocation strategies for system management in the datacenter. In most of the previous work, a controller employs a performance model of the system to make decisions about the optimal allocation of resources. However, these models are usually trained offline or on a small-scale deployment and will not accurately capture the performance of the controlled application. To achieve accurate control of the web application, the models need to be trained directly on the production system and adapted to changes in workload and performance of the application. In this paper we propose to train the performance model using an exploration policy that quickly collects data from different performance regimes of the application. The goal of our approach for managing the exploration process is to strike a balance between not violating the performance SLAs and the need to collect sufficient data to train an accurate performance model, which requires pushing the system close to its capacity. We show that by using our exploration policy, we can train a performance model of a Web 2.0 application in less than an hour and then immediately use the model in a resource allocation controller

The SCADS Director: Scaling a Distributed Storage System Under Stringent Performance Requirements

Elasticity of cloud computing environments provides an economic incentive for automatic resource allocation of stateful systems running in the cloud. However, these systems have to meet strict performance Service-Level Objectives (SLOs) expressed using upper percentiles of request latency, such as the 99th. Such latency measurements are very noisy, which complicates the design of the dynamic resource allocation. We design and evaluate the SCADS Director, a control framework that reconfigures the storage system on-the-fly in response to workload changes using a performance model of the system. We demonstrate that such a framework can respond to both unexpected data hotspots and diurnal workload patterns without violating strict performance SLOs.