Lennart Paul, Jorge-Humberto Urrea-Quintero, Umer Fiaz
et al.
Deep geological repositories are critical for the long-term storage of hazardous materials, where understanding the mechanical behavior of emplacement drifts is essential for safety assurance. This study presents a surrogate modeling approach for the mechanical response of emplacement drifts in rock salt formations, utilizing Gaussian Processes (GPs). The surrogate model serves as an efficient substitute for high-fidelity mechanical simulations in many-query scenarios, including time-dependent sensitivity analyses and calibration tasks. By significantly reducing computational demands, this approach facilitates faster design iterations and enhances the interpretation of monitoring data. The findings indicate that only a few key parameters are sufficient to accurately reflect in-situ conditions in complex rock salt models. Identifying these parameters is crucial for ensuring the reliability and safety of deep geological disposal systems.
In the transition to achieving net zero emissions, it has been suggested that a substantial expansion of electric power grids will be necessary to support emerging renewable energy zones. In this paper, we propose employing battery-based feedback control and nonlinear negative imaginary (NI) systems theory to reduce the need for such expansion. By formulating a novel Luré-Postnikov-like Lyapunov function, stability results are presented for the feedback interconnection of two single nonlinear NI systems, while output feedback consensus results are established for the feedback interconnection of two networked nonlinear NI systems based on a network topology. This theoretical framework underpins our design of battery-based control in power transmission systems. We demonstrate that the power grid can be gradually transitioned into the proposed NI systems, one transmission line at a time.
Frederik Baymler Mathiesen, Sofie Haesaert, Luca Laurenti
This paper introduces a novel abstraction-based framework for controller synthesis of nonlinear discrete-time stochastic systems. The focus is on probabilistic reach-avoid specifications. The framework is based on abstracting a stochastic system into a new class of robust Markov models, called orthogonally decoupled Interval Markov Decision Processes (odIMDPs). Specifically, an odIMDPs is a class of robust Markov processes, where the transition probabilities between each pair of states are uncertain and have the product form. We show that such a specific form in the transition probabilities allows one to build compositional abstractions of stochastic systems that, for each state, are only required to store the marginal probability bounds of the original system. This leads to improved memory complexity for our approach compared to commonly employed abstraction-based approaches. Furthermore, we show that an optimal control strategy for a odIMDPs can be computed by solving a set of linear problems. When the resulting strategy is mapped back to the original system, it is guaranteed to lead to reduced conservatism compared to existing approaches. To test our theoretical framework, we perform an extensive empirical comparison of our methods against Interval Markov Decision Process- and Markov Decision Process-based approaches on various benchmarks including 7D systems. Our empirical analysis shows that our approach substantially outperforms state-of-the-art approaches in terms of both memory requirements and the conservatism of the results.
Thomas G. Kelly, Mohammad D. Soorati, Klaus-Peter Zauner
et al.
Swarm robotics is a study of simple robots that exhibit complex behaviour only by interacting locally with other robots and their environment. The control in swarm robotics is mainly distributed whereas centralised control is widely used in other fields of robotics. Centralised and decentralised control strategies both pose a unique set of benefits and drawbacks for the control of multi-robot systems. While decentralised systems are more scalable and resilient, they are less efficient compared to the centralised systems and they lead to excessive data transmissions to the human operators causing cognitive overload. We examine the trade-offs of each of these approaches in a human-swarm system to perform an environmental monitoring task and propose a flexible hybrid approach, which combines elements of hierarchical and decentralised systems. We find that a flexible hybrid system can outperform a centralised system (in our environmental monitoring task by 19.2%) while reducing the number of messages sent to a human operator (here by 23.1%). We conclude that establishing centralisation for a system is not always optimal for performance and that utilising aspects of centralised and decentralised systems can keep the swarm from hindering its performance.
This article considers the problem of conflict-free distribution of point-sized agents on a circular periphery encompassing all agents. The two key elements of the proposed policy include the construction of a set of convex layers (nested convex polygons) using the initial positions of the agents, and a novel search space region for each of the agents. The search space for an agent on a convex layer is defined as the region enclosed between the lines passing through the agent's position and normal to its supporting edges. Guaranteeing collision-free paths, a goal assignment policy designates a unique goal position within the search space of an agent at the initial time itself, requiring no further computation thereafter. In contrast to the existing literature, this work presents a one-shot, collision-free solution to the circular distribution problem by utilizing only the initial positions of the agents. Illustrative examples and extensive Monte-Carlo studies considering various practical attributes demonstrate the effectiveness of the proposed method.
The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient descent methods offer strong theoretical guarantees for convex problems; however, they demand meticulous hyperparameter tuning for non-convex ones. The emerging paradigm of learning to optimize (L2O) automates the discovery of algorithms with optimized performance leveraging learning models and data - yet, it lacks a theoretical framework to analyze convergence of the learned algorithms. In this paper, we fill this gap by harnessing nonlinear system theory. Specifically, we propose an unconstrained parametrization of all convergent algorithms for smooth non-convex objective functions. Notably, our framework is directly compatible with automatic differentiation tools, ensuring convergence by design while learning to optimize.
Samaneh Nojoumi Siahmard, Mehdi Meftah Halaghi, Mahdi Esmaeili Varaki
et al.
The understanding of the mechanisms underlying the diffusion, advection, and mixing of pollutants constitutes a crucial aspect in monitoring water resources quality, particularly in the context of river systems. The longitudinal dispersion coefficient holds significant importance in predicting and illustration of pollutants in river systems. The current study investigated the advection and dispersion of pollution in meandering rivers experimentally. A series of experiments were undertaken to quantify the tracer concentration within a laboratory flume. The meandering flume with 8 bends was used to consider variation of tracer concentration for different discharges and solid and sedimentary beds. Utilizing the collected experimental data and employing the routing process, the longitudinal dispersion coefficient was determined across various segments of meandering rivers. Comparison of results for both solid and sedimentary bed indicates that as the flow discharge increases, the tracer's transfer speed increases while the transfer time decreases. Consequently, the difference between the peak concentration of tracer on the hydrographs (Cr) of input and output sections in the range of 21 to 100 percent decreases. Furthermore, by changing the bed from solid to sedimentary, Cr and the longitudinal mixing coefficient (DL) increased noticeably. In the mentioned conditions, the lowest values of Cr and DL parameters were obtained with 100 and 85% increase in value, respectively. Additionally, the results revealed a positive correlation between the mixing parameters and the length of the traversed tracer along the bends. The analysis of the data indicated that as the Reynolds number increased, the longitudinal mixing coefficient increased in both solid and sedimentary bed.
Technology, Water supply for domestic and industrial purposes
Nafiseh Panahi Osalou, Lobat Taghavi, Amir Hessam Hassani
et al.
The effluents of polymerization plants are acidic due to the use of sulfuric acid as flocculation agent and their wastewater contains high amounts of sulfate ions. In wastewater industry, several physical, chemical and biological treatment methods are used. The main purpose of this study is to examine the feasibility of anaerobic biological treatment of sulfate in industrial effluents by using sulfate-reducing bacteria. The research method is quantitative, and experiments and data collection from 2017-2020. The main variables of this research are temperature, effluent pH and the population of microorganisms. Experiments at two temperature levels of 25 and 60 oC and two different pHs, 7.5 and 8.5, were performed and four series of experiments were done. The results showed that by increasing the temperature of the solution from 25° to 60 °C at a concentration of 50 mg/L sulfate ion and a pH of 7.5, microorganisms showed 17.6% better performance. Also, the performance of microorganisms in anaerobic biological treatment at concentration of 50 mg/L of sulfate ion was 45.3% minimum and 49.9% at maximum. Comparison of experimental results at two different pHs of 7.5 and 8.5, indicates that at the same temperatures of 25 and 65°C, with increasing pH, the performance of microorganisms has improved by 16.4%. The efficiency of wastewater treatment increases 19.6% by changing pH from 7.5 to 8.5. Results showed that the correlation between temperature and sulfate ion concentration follows the 1st degree equation. Also, the weak pH environment provides suitable conditions for the removal of ions in the effluents, and the correlation between increasing the pH of the solution and decreasing the concentration of sulfate ions is a 2nd degree equation. Study showed that temperature and pH are the two effective factors in the process of biological treatment of effluents.
Technology, Water supply for domestic and industrial purposes
We present a data-driven framework based on Lyapunov theory to provide stability guarantees for a family of hybrid systems. In particular, we are interested in the asymptotic stability of switching linear systems whose switching sequence is constrained by labeled graphs, namely constrained switching linear systems. In order to do so, we provide chance-constrained bounds on stability guarantees, that can be obtained from a finite number of noisy observations. We first present a method providing stability guarantees from sampled trajectories in the hybrid state-space of the system. We then study the harder situation where one only observes the continuous part of the hybrid states. We show that in this case, one may still obtain formal chance-constrained stability guarantees. For this latter result we provide a new upper bound of general interest, also for model-based stability analysis
Seyyed Mohamad Sadati Tilebon, Saber Babaee Zadvarzi, Vahid Sadeghi
et al.
Nowadays, decreasing access to sustainable water sources has pushed the water shortage to water stress and water crisis in some cases. This phenomenon has led to more and more researchers and craftsmans’ efforts to achieve cost-effective commercial processes for a sustainable supply of water. Reverse osmosis process showed suitable potential for supplying the human’s required drinking water among all the water treatment processes. However, this process needs economic studies in macro-industrial levels. Neka power plants’ reverse osmosis desalination of seawater has been designed for production of 6,000 m3/day desalinated water. Feed water of this plant is supplied from the Caspian Sea with total dissolved solids of 15,000 mg/L and electrical conductivity of 20,000 µS/cm. Based on the results, required capital cost of this plant is $6 million and annual variable cost of $1.232 million is needed for desalination plant operation. Final fixed price of the desalinated water has been calculated $0.684 per cubic meter of desalinated water with the consideration of 20 years’ plant life cycle. Break-even point of the desalination plant has been obtained less than 6 years and less than 2 years with sales price of 1 $/m3 and 2 $/m3 of desalinated water, respectively. Results show that reverse osmosis based desalination systems are a suitable replacement for conventional freshwater sources.
Technology, Water supply for domestic and industrial purposes
Moslem Shahamatpour, Seyed Mostafa Tabatabaee Ghomsheh, Sara Maghsoudi
et al.
In recent years, with the sudden rise in water prices, many industries have decided to treat their effluent and reuse this water. In order to treat the wastewater produced by Abadan Oil Refining Company and turn it into water that can be used in this industrial, nanofiltration membrane treatment or more advanced processes are necessary. The nanofiltration membrane has a limit of hydrocarbon compounds in its input feed up to a maximum amount of 3 mg/L, but the effluent used in this research contains 5.1 mg/L of hydrocarbons. In this research, the pre-treatment process was done by anthracite adsorbent in a fixed bed, and the variables of the adsorption process, including reactor flow rate, adsorbent service time, and pH were optimized to maximize the amount of adsorption. The optimum flow rate of the reactor is 8.48 L/min, time 92.9 minutes, pH: 6.36 and the highest percentage of hydrocarbon removal is 57.62%. The output of the adsorption process is 2.16 mg/L of hydrocarbon compounds, which is considered as the feed of the nanofiltration process. The module used in this process is disk type and polyamide membrane. In the nanofiltration process, the optimal value of the variable pressure was 9.58 barg, temperature was 18.04 °C, pH: 4.62 and the highest removal percentage was 81.35%. Combining the two processes of adsorption and nanofiltration, they were able to produce hydrocarbon compounds at the output of the aqueous nanofiltration stage with a value of 0.4 mg/L, which can be used in several internal networks of the refinery. Each process has three variables, each of which is examined in 5 levels and the number of experiments in each step is 20 and the optimization of variables in all stages using Design Expert software using the response surface methodology based on the principles of central composite design Mathematical model and optimization have been used for data design.
Technology, Water supply for domestic and industrial purposes
Cyber-physical systems (CPSs) are often complex and safety-critical, making it both challenging and crucial to ensure that the system's specifications are met. Simulation-based falsification is a practical testing technique for increasing confidence in a CPS's correctness, as it only requires that the system be simulated. Reducing the number of computationally intensive simulations needed for falsification is a key concern. In this study, we investigate Bayesian optimization (BO), a sample-efficient approach that learns a surrogate model to capture the relationship between input signal parameterization and specification evaluation. We propose two enhancements to the basic BO for improving falsification: (1) leveraging local surrogate models, and (2) utilizing the user's prior knowledge. Additionally, we address the formulation of acquisition functions for falsification by proposing and evaluating various alternatives. Our benchmark evaluation demonstrates significant improvements when using local surrogate models in BO for falsifying challenging benchmark examples. Incorporating prior knowledge is found to be especially beneficial when the simulation budget is constrained. For some benchmark problems, the choice of acquisition function noticeably impacts the number of simulations required for successful falsification.
Soha Nozhat, Amirhesam Hasani, Homayon Ahmad Panahi
et al.
The entry of herbicides into drinking water supply sources can have devastating effects on human health and the environment. Therefore, removal of them from the aquatic environment is essential, in order to preserve the environment. Therefore, this study was conducted with the aim to investigate the isotherm absorption of graphene oxide modified by organic dendrimers to remove Butachlor toxin from the aquatic environment. In the present study, operating magnetic graphene oxide was produced by absorption of covalent bonds and used as adsorbent. Synthetic adsorbent properties were analyzed by FTIR, XRD, SEM, TEM, TGA, VSM and EDS. Also, the effects of pH parameters, contact time, contaminant concentration, adsorbent amount, temperature and reusability on adsorption absorption capacity were investigated and optimal conditions were determined. The absorption results were described by Langmuir, Freundlich, Temkin and kinetic adsorption models by first-order and quasi-second-order models and thermodynamic equations. The results indicated that functionalized graphene oxide effectively absorbs Butachlor and absorption percentage is significantly affected by the examined parameters. By increasing the time to 45 minutes, increasing the pH to 5, increasing the amount of adsorbent to 3 g/L and the concentration of Butachlor toxin to 10 mg/L and increasing the temperature to 25 ⁰C, the rate of absorption of Butachlor toxin has increased to 95.4%. Toxin absorption increased from 37 to 50 ⁰C, and after ten re-uses of the adsorbent, the absorption rate decreased by only 6.5%. Under optimal conditions, the adsorbent was able to remove 86.3% of Butachlor toxin in the real sample with a standard deviation of 6.06%. The Langmuir isotherm described the absorption process well (R2 = 0.99). Synthesized nano-adsorbent is an efficient, powerful and heat-sensitive adsorbent for removing Butachlor from the aquatic environment.
Technology, Water supply for domestic and industrial purposes
Based on the analysis of geometric and hydraulic characteristics of pressureless water channels cross sections, recommendations for parameters calculation of these systems with an arbitrary shape of their cross section were elaborated in this article. Elements of hydraulic modeling and analogy were used in the preparation of the presented material. The considered pressureless channels are widely used in rainwater disposal systems designing and can also be used in the designing of other special water disposal networks. Well-known theoretical and empirical hydraulic dependences, relevant reference data, in particular, discharge and velocity characteristics, local resistance coefficients were used in this article. Necessary information on selection of water disposal wells is given. The proposed method of calculation the parameters of water disposal channels can be used in different fluid flow modes: both pressureless and pressure, steady and unsteady. It is recommended for use in the water disposal networks designing, regardless of the pipelines material.
Sandor Beregi, David A. W. Barton, Djamel Rezgui
et al.
Augmenting mechanistic ordinary differential equation (ODE) models with machine-learnable structures is an novel approach to create highly accurate, low-dimensional models of engineering systems incorporating both expert knowledge and reality through measurement data. Our exploratory study focuses on training universal differential equation (UDE) models for physical nonlinear dynamical systems with limit cycles: an aerofoil undergoing flutter oscillations and an electrodynamic nonlinear oscillator. We consider examples where training data is generated by numerical simulations, whereas we also employ the proposed modelling concept to physical experiments allowing us to investigate problems with a wide range of complexity. To collect the training data, the method of control-based continuation is used as it captures not just the stable but also the unstable limit cycles of the observed system. This feature makes it possible to extract more information about the observed system than the open-loop approach (surveying the steady state response by parameter sweeps without using control) would allow. We use both neural networks and Gaussian processes as universal approximators alongside the mechanistic models to give a critical assessment of the accuracy and robustness of the UDE modelling approach. We also highlight the potential issues one may run into during the training procedure indicating the limits of the current modelling framework.
Behavior trees represent a hierarchical and modular way of combining several low-level control policies into a high-level task-switching policy. Hybrid dynamical systems can also be seen in terms of task switching between different policies, and therefore several comparisons between behavior trees and hybrid dynamical systems have been made, but only informally, and only in discrete time. A formal continuous-time formulation of behavior trees has been lacking. Additionally, convergence analyses of specific classes of behavior tree designs have been made, but not for general designs. In this letter, we provide the first continuous-time formulation of behavior trees, show that they can be seen as discontinuous dynamical systems (a subclass of hybrid dynamical systems), which enables the application of existence and uniqueness results to behavior trees, and finally, provide sufficient conditions under which such systems will converge to a desired region of the state space for general designs. With these results, a large body of results on continuous-time dynamical systems can be brought to use when designing behavior tree controllers.
Designing the optimal linear quadratic regulator (LQR) for a large-scale multi-agent system (MAS) is time-consuming since it involves solving a large-size matrix Riccati equation. The situation is further exasperated when the design needs to be done in a model-free way using schemes such as reinforcement learning (RL). To reduce this computational complexity, we decompose the large-scale LQR design problem into multiple smaller-size LQR design problems. We consider the objective function to be specified over an undirected graph, and cast the decomposition as a graph clustering problem. The graph is decomposed into two parts, one consisting of independent clusters of connected components, and the other containing edges that connect different clusters. Accordingly, the resulting controller has a hierarchical structure, consisting of two components. The first component optimizes the performance of each independent cluster by solving the smaller-size LQR design problem in a model-free way using an RL algorithm. The second component accounts for the objective coupling different clusters, which is achieved by solving a least squares problem in one shot. Although suboptimal, the hierarchical controller adheres to a particular structure as specified by inter-agent couplings in the objective function and by the decomposition strategy. Mathematical formulations are established to find a decomposition that minimizes the number of required communication links or reduces the optimality gap. Numerical simulations are provided to highlight the pros and cons of the proposed designs.
In this paper, we consider the problem of invariant set computation for black-box switched linear systems using merely a finite set of observations of system trajectories. In particular, this paper focuses on polyhedral invariant sets. We propose a data-driven method based on the one step forward reachable set. For formal verification of the proposed method, we introduce the concepts of $λ$-contractive sets and almost-invariant sets for switched linear systems. The convexity-preserving property of switched linear systems allows us to conduct contraction analysis on the computed set and derive a probabilistic contraction property. In the spirit of non-convex scenario optimization, we also establish a chance-constrained guarantee on set invariance. The performance of our method is then illustrated by numerical examples.
Kenneth Lai, Svetlana N. Yanushkevich, Vlad Shmerko
This paper addresses the evaluation of the performance of the decision support system that utilizes face and facial expression biometrics. The evaluation criteria include risk of error and related reliability of decision, as well as their contribution to the changes in the perceived operator's trust in the decision. The relevant applications include human behavior monitoring and stress detection in individuals and teams, and in situational awareness system. Using an available database of cross-spectral videos of faces and facial expressions, we conducted a series of experiments that demonstrate the phenomenon of biases in biometrics that affect the evaluated measures of the performance in human-machine systems.
Network reliability is one of the most important parameter when evaluating the efficiency of water distribution networks (WDNs). WDN reliability is calculated based on the mechanical, hydraulic and water quality aspects under normal and also abnormal conditions such as system failures. In this paper, hydraulic reliability was calculated through the ratio of satisfied nodal demands. In this regard, hydraulic simulation is performed based on the proposed EPANET-IMNO algorithm based on a Pressure-Driven Simulation model. This algorithm is written in Visual Studio through C<sup>++</sup> code. The mechanical reliability of the network was evaluated by means of BDD algorithm based on probability of having a connection between the source nodes and consumption nodes. DNA and RNA tools are used to evaluate the mechanical reliability based on BDD method. Finally, an integrated reliability is proposed for optimum design and operation of WDNs. A looped WDN with nine nodes and a branched WDN were considered in this paper. The results showed that besides the hydraulic reliability the evaluation of mechanical reliability index is very important in the design of WDN to improve the operation of WDNs. Adding 4 loops to the branched WDN increased its reliability by 18.3%. Also, it was determined that under a looped WDN eliminating one pipe in the worst case may reduce the network reliability by28%.
Technology, Water supply for domestic and industrial purposes