Sustainable Energy Management Benchmark at Wastewater Treatment Plant

Urban wastewater effluents bring large amounts of nutrients, organic matter, and organic microcontaminants into freshwater ecosystems. Ensuring the quality of wastewater treatment (WWT) is one of the main challenges facing the management of wastewater treatment plants (WWTPs). However, achievement of high-quality standards leads towards significant energy consumption: usually the more intensive WWT process requires additional energies. Energy efficiency at WWTP is actual mainstream on the current sustainable development agenda. The WWTP processes and methods can be considered from the standpoint of material and energy flows according to circular economy paradigm, which offers great possibilities to reuse waste originating from WWT in order to receive renewable energy. The correlation between energy and quality issues to evaluate WWTP efficiency is of a great scientific and practical interest. The main goal of the paper is to check the dependency between these two main issues in WWTP management—WWT quality and energy efficiency—and to determine possible limits of such relation. The municipal sewerage system of Ekaterinburg, Russia was studied within this paper. The total length of centralized sewerage system in Ekaterinburg is over 1500 km of pipes within two main sewerage basins: northern and southern. The methodological framework for the current research consisted of three steps: (i) WWT quality evaluation, (ii) energy efficiency evaluation, and (iii) WWTP Quality/Energy (Q/E) efficiency dependency matrix. For the purpose of research, authors investigated the 2015–2018 period. The results showed that the outputs correlate with the technical conditions of WWTPs and the implementation of the best available techniques (BATs): most of the northern WWTP values are referred to the green zone (good rank), while the southern WWTP values are situated generally in the orange zone (unsatisfactory rank). The proposed methodological approach for Q/E dependency of WWT process creates a strong but simple tool for managers to evaluate the current success of the operation of WWTP and progress towards circular economy practices implementation.

Decoupling Strategy and Modular Design for Loosely Coupled Organizations

Under the technological innovation network, loosely coupled organizations featured by weak connections and relative independence have formed through the dynamic division of labor and knowledge sharing of enterprises. The loosely coupled organizations greatly enhance cooperation performance, but face problems such as poor flexible matching between partners and low innovation efficiency. Meanwhile, modular organizations can connect with other organizations to realize a specific operational goal via contracts and carry prominent features such as collaborative innovation, adaptive division of labor, and dynamic capabilities, offering an effective network governance model for collaboration between enterprises. Based on the two dimensions (looseness and coupling) between organizations, this paper uses the design dependency matrix (DDM) to map demands for functional/design modules, under the division of labor within products and the decisions on product modules, and preliminarily decouple the functional/design modules. Based on clustering DDM, a decision structure matrix (DSM) was constructed, and the clustering decoupling algorithm was adopted to create clustering design structure modules. Referring to these clustering modules, intraproduct division of labor was performed to realize the modular cooperation between organizations.

A Microservice Regression Testing Selection Approach Based on Belief Propagation

Regression testing is required in each iteration of microservice systems. Regression testing selection, which reduces testing costs by selecting a subset from the original test cases, is one of the main techniques to optimize regression testing. Existing techniques mainly rely on the information retrieved from artifacts such as code files and system models. For microservice systems with service autonomy, development method diversity and a large amount of services, such artifacts are too difficultly obtained and costly processed to apply those approaches. This paper presents a regression testing selection approach called MRTS-BP, which needs the API gateway layer logs instead of code files and system models as inputs. By parsing the API gateway layer logs, our approach establishes the service dependency matrix, which in further is transformed into a directed graph with the services as nodes. Then, to find out which test cases are affected by service changes, an algorithm based on belief propagation is presented to compute the quantitative results of service-change propagation from the directed graph. Finally, the relationships between original test cases and service-change propagation results are established to select test cases with three strategies. To evaluate the efficiency of MRTS-BP, the empirical study based on four microservice systems is presented. A typical technique RTS-CFG is compared with MRTS-CFG and four experiments are setup to investigate four research questions. The results show that MRTS-BP can not only reduce the number of test cases by half compared with the retest-all strategy while ensuring the safety, but also save at least 20% testing time costs more than that of RTS-CFG. MRTS-BP is more practical than the techniques relying on the artifacts when the latter cannot be implemented due to the artifacts are difficult to obtain and process.

MATRIX OF DEPENDENCE OF MUNICIPALITIES IN THE NORTH REGION OF GOIÁS

Of the total number of municipalities in the northern region of the State of Goiás, part of them have their own revenues below requirements, if current conditions are maintained, they tend to be incorporated into neighboring municipalities from 2025 onwards in compliance with PEC 188/2019.Would there be an alternative for municipal development and sustainability without the necessary population increase? In this sense, the purpose was to identify the economic and social weaknesses that characterize the municipalities in the northern region and to create a “Dependency Matrix”. The methodology consisted of consulting databases of federal, state and municipal agencies and programs. The results demonstrate the low level of economic activity, own revenues, high dependence on external revenues, high payroll on civil clerk, exposing themselves to the dictates of the Fiscal Responsibility Law (LRF) and high dependence on the Income Transfer Programs of the Federal Government such as the Bolsa Família Program (PBF) and the Continuous Payment Benefit (BPC). The Dependency Matrix assists the municipal manager in restraining the alleged migratory flight, the loss of economic relevance, as well as economic, environmental and social sustainability. Keywords: Dependency Matrix. Economy. Sustainability.

A Comprehensive Evaluation Method of Sensor Selection for PHM Based on Grey Clustering

Sensor selection plays an essential and fundamental role in prognostics and health management technology, and it is closely related to fault diagnosis, life prediction, and health assessment. The existing methods of sensor selection do not have an evaluation standard, which leads to different selection results. It is not helpful for the selection and layout of sensors. This paper proposes a comprehensive evaluation method of sensor selection for prognostics and health management (PHM) based on grey clustering. The described approach divides sensors into three grey classes, and defines and quantifies three grey indexes based on a dependency matrix. After a brief introduction to the whitening weight function, we propose a combination weight considering the objective data and subjective tendency to improve the effectiveness of the selection result. Finally, the clustering result of sensors is obtained by analyzing the clustering coefficient, which is calculated based on the grey clustering theory. The proposed approach is illustrated by an electronic control system, in which the effectiveness of different methods of sensor selection is compared. The result shows that the technique can give a convincing analysis result by evaluating the selection results of different methods, and is also very helpful for adjusting sensors to provide a more precise result. This approach can be utilized in sensor selection and evaluation for prognostics and health management.

Improved Text Mining Algorithm for Fault Detection using Combined D-Matrix

Systematic diagnostic version of Fault dependency (D-matrix) mostly use for setup the fault method records and its contributing courting on the classified system-degree. It includes dependencies and association between recognizable failure approaches and signs and symptoms related to a machine. Proposed system in this paper describes an relations of domain primarily based textual content repository for construction and renovate combined data dependency matrix through mining lacks of the tuple exact unstructured text ,cumulative during the analysis incidents. Here paradigm is combined D matrix and then fault analysis through textual content mining using advance data preprocessing technique approach to pick out dependencies. Using real-existence statistics accumulated and validated in proposed method

Quantifying the complexity of subassemblies in a fully automated assembly system

Purpose Complexity is the main challenge for present and future manufacturers. Assembly complexity heavily affects a product’s final quality in the fully automated assembly system. This paper aims to propose a new method to assess the complexity of modern automated assembly system at the assembly design stage with respect to the characteristics of both manufacturing system and each single component to be mounted. Aiming at validating the predictive model, a regression model is additionally presented to estimate the statistic relationship between the real assembly defect rate and predicted complexity of the fully automated assembly system. Design/methodology/approach The research herein extends the S. N. Samy and H. A. ElMaraghy’s model and seeks to redefine the predictive model using fuzzy evaluation against a fully automated assembly process at the assembly design stages. As the evaluation based on the deterministic scale with accurate crisp number can hardly reflect the uncertainty of the judgement, fuzzy linguistic variables are used to measure the interaction among influence factors. A dependency matrix is proposed to estimate the assembly complexity with respect to the interactions between mechanic design, electric design and process factors and main functions of assembly system. Furthermore, a complexity attributes matrix of single part is presented, to map the relationship between all individual parts to be mounted and three major factors mentioned in the dependency matrix. Findings The new proposed model presents a formal quantification to predict assembly complexity. It clarifies that how the attributes of assembly system and product components complicate the assembly process and in turn influence the manufacturing performance. A center bolt valve in the camshaft of continue variable valve timing is used to demonstrate the application of the developed methodology in this study. Originality/value This paper presents a developed method, which can be used to improve the design solution of assembly concept and optimize the process flow with the least complexity.

Reversible Circuits Synthesis from Functional Decision Diagrams by using Node Dependency Matrices

Decision diagrams are a data structure suitable for reversible circuit synthesis. Functional decision diagrams (FDDs) are particularly convenient in synthesis with Toffoli gates, since the functional expressions for decomposition rules used in them are similar to the functional expressions of Toffoli gates. The main drawback of reversible circuit synthesis based on decision diagrams is the usually large number of ancilla lines. This paper presents two methods for the reduction of the number of ancilla lines in reversible circuits derived from FDDs by selecting the order of implementation of nodes. In the first method, nodes are implemented by levels, starting from the bottom level to the top. The method uses appropriately defined level dependency matrices for choosing the optimal order of implementation of nodes at the same level. In this way, the optimization is performed level by level. The second method uses a diagram dependency matrix expressing mutual dependencies among all the nodes in the diagram. This method is computationally more demanding than the first method, but the reductions of both the number of lines and the Quantum cost of the circuits are larger.