Deliberative architecture for smart sensors in the filtering operation of a water purification plant

The increase of applications for industrial smart sensors is booming, mainly due to the use of distributed automation architectures, industrial evolution and recent technological advances, which guide the industry to a greater degree of automation, integration and globalization. In this research work, an architecture for deliberative-type intelligent industrial sensors is proposed, based on the BDI (Belief Desire Intentions) model, adaptable to the measurement of different variables of the filtering process of a water purification plant. An intelligent sensor with functions of signal digitalization, self-calibration, alarm generation, communication with PLC, user interface for parameter adjustment, and analysis with data extrapolation have been arranged. For decision making, the use of fuzzy logic techniques has been considered, which allows imprecise parameters to be appropriately represented, simplifying decision problem solving in the industrial environment, generating stable and fast systems with low processing requirements. The proposed architecture has been modelled, simulated and validated using UML language in conjunction with Petri nets, which facilitate the representation of discrete system events, presenting them clearly and precisely. In the implementation and testing of the prototype, C/C ++ language has been used in an 8-bit microcontroller, experimentally corroborating the operation of the device, which allowed evaluating the behavior of a pseudo-intelligent agent based on the requirements of the water treatment plant, and also through comparisons with similar works developed by other researchers

An interoperable workflow-based framework for the automation of building intelligent process control systems

One of the major problems to design and implement a control/supervision system for a process lies in the need to establish an ad-hoc system for each process installation. On the other side, an open challenge related to the deployment of Intelligent Decision Support Systems (IDSSs) is the interoperability of the different methods used, in order to allow interaction and reuse of different data mining methods and the use of methods based on a model or an expert. Thus, this paper proposes the use of visual workflows, to enable the automation of the design task and the implementation of Intelligent Process Control Systems (IPCSs). The framework will allow the user to specify the design and control of a concrete process as well as the required data-driven and expert models using a graphical workflow environment. The framework is based on a three-layer architecture: first, a comprehensive data science flow description layer (dataflow layer) to produce/discover data-driven models from process data; second, a flowchart of the different components of the process (process-design flow layer) to obtain a simulation model from the design. Finally, the on-line IPCS (process control workflow layer), where the different data-driven models, expert-based models and intelligent reasoning methods interoperate to control and supervise the process. Thus, the resulting system can automatically generate both simulation models of the process and programming code to control and supervise the process, using workflows designed for each particular installation. The case study is focused on the supervision of a Wastewater Treatment Plant (WWTP) located in the Barcelona region.The authors acknowledge the partial support of this work by the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (author's final draft

The Responsibility Quantification (ResQu) Model of Human Interaction with Automation

Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation