Mixed-integer linear methods for layout-optimization of screening systems in recovered paper production

The industrial treatment of waste paper in order to regain valuable fibers from which recovered paper can be produced, involves several steps of preparation. One important step is the separation of stickies that are normally attached to the paper. If not properly separated, remaining stickies reduce the quality of the recovered paper or even disrupt the production process. For the mechanical separation process of fibers from stickies a separator screen is used. This machine has one input feed and two output streams, called the accept and the reject. In the accept the fibers are concentrated, whereas the reject has a higher concentration of stickies. The machine can be controlled by setting its reject rate. But even when the reject rate is set properly, after just a single screening step, the accept still has too many stickies, or the reject too many fibers. To get a better separation, several separators have to be assembled into a network. From a mathematical point of view this problem can be seen as a multi-commodity network flow design problem with a nonlinear, controllable distribution function at each node. We present a nonlinear mixed-integer programming model for the simultaneous selection of the network’s topology and the optimal setting of each separator. Numerical results are obtained via different types of linearization of the nonlinearities and the use of mixed-integer linear solvers, and compared with state-of-the-art global optimization software.     

An Efficient Reliability Evaluation Approach for Networks with Simultaneous Multiple‐Node‐Pair Flow Requirements

This paper proposes an efficient approach for reliability evaluation of multiple‐sources and multiple‐destinations flow networks. The proposed approach evaluates multiple node pair capacity related reliability. The proposed approach is a three‐step approach; in the first step, it enumerates network minimal cut sets. These network minimal cut sets are then used to enumerate subset cuts in the second step. Third step involves the evaluation of multiple node pair capacity related reliability from the enumerated subset cuts using a multi‐variable inversion sum‐of‐disjoint set approach. The proposed approach can be used for optimal network design as it combines multiple performance requirements, that is, flow requirements between multiple node pairs and network reliability, in a single criterion. Copyright © 2016 John Wiley & Sons, Ltd.     

Linear program-based algorithm for the optimal design of wastewater treatment systems

This paper presents a new approach for the optimal design of distributed wastewater treatment networks with multiple contaminants. It consists of a two-stage solution strategy. In the first stage, a decomposition method is employed that replaces the general non-linear program (NLP) by a succession of linear programs, one for each treatment unit. In the second stage, the resulting network is used as a starting point for the solution of the general NLP by a local optimization solver. The decomposition process considers a specific substructure, where it is assumed that the wastewater streams go through the treatment units in sequence. To consider all combinations, the two-stage solution strategy is applied as many times as the number of possible sequences. This allows considering multiple and structurally different starting points, thus increasing the probability of finding global optimal solutions. The results have shown that the proposed approach can find better solutions than other approach reported in the literature, however with a drawback of being more demanding computationally. An erratum to this article can be found at     

Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

Parkinson’s disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’ voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers. Results were highly successful, with 90% accuracy produced by the random forest classifier and 81.5% by the logistic regression classifier. Furthermore, a deep neural network was implemented to investigate if such variation in method could add to the findings. It proved to be effective, as the neural network yielded an accuracy of nearly 92%. Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’ voices. This research calls for a revolutionary diagnostic approach in decision support systems, and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.     

Process integration design methods for water conservation and wastewater reduction in industry

This three part series of papers addresses operational techniques for applying mass integration principles to design in industry with special focus on water conservation and wastewater reduction. Part 1 covers design techniques for any number of wastewater streams containing a single contaminant. The technique comprises a two stage graphical approach. In the first stage, the water pinch diagram is used to identify key design targets (such as the minimum freshwater requirement of the studied system, the amount of achievable water recycling and reuse and the water quality concentration bottleneck) for the industrial process of interest. Key practical insights provided by the water pinch diagram are discussed. In the second stage, source–sink mapping diagrams are used to identify the water recycling and reuse network, and any alternative networks, that achieve the identified targets. A case study is included to illustrate the proposed methodology. Electronic Publication     

Linear approximation model network and its formation via evolutionary computation

To overcome the deficiency of ’local model network’ (LMN) techniques, an alternative ’linear approximation model’ (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked through output or parameter interpolation. The linear models are valid for the entire operating trajectory and hence overcome the local validity of LMN models, which impose the predetermination of a scheduling variable that predicts characteristic changes of the nonlinear system. LAMs can be evolved from sampled step response data directly, eliminating the need for local linearisation upon a pre-model using derivatives of the nonlinear system. The structural difference between a LAM network and an LMN is that the overall model of the latter is a parameter-varying system and hence nonlinear, while the former remains linear time-invariant (LTI). Hence, existing LTI and transfer function theory applies to a LAM network, which is therefore easy to use for control system design. Validation results show that the proposed method offers a simple, transparent and accurate multivariable modelling technique for nonlinear systems.     

Optimal cost design of water distribution networks using a decomposition approach

Water distribution network decomposition, which is an engineering approach, is adopted to increase the efficiency of obtaining the optimal cost design of a water distribution network using an optimization algorithm. This study applied the source tracing tool in EPANET, which is a hydraulic and water quality analysis model, to the decomposition of a network to improve the efficiency of the optimal design process. The proposed approach was tested by carrying out the optimal cost design of two water distribution networks, and the results were compared with other optimal cost designs derived from previously proposed optimization algorithms. The proposed decomposition approach using the source tracing technique enables the efficient decomposition of an actual large-scale network, and the results can be combined with the optimal cost design process using an optimization algorithm. This proves that the final design in this study is better than those obtained with other previously proposed optimization algorithms.     

Multivariate statistical process control with artificial contrasts

A multivariate control region can be considered to be a pattern that represents the normal operating conditions of a process. Reference data can then be generated and used to learn the difference between this region and random noise. Then multivariate statistical process control can be converted to a supervised learning task. This can dramatically reshape the control region and open the control problem to a rich collection of supervised learning tools. Such tools provide generalization error estimates that can be used to specify error rates. The effectiveness of such an approach is shown here. Such a computational approach is now easily accomplished with modern computing resources. Examples use random forests and a regularized least squares classifier as the learners.     

Efficient implementation of vector Preisach-type models using orthogonally coupled hysteresis operators

Vector hysteresis models are regarded as helpful tools that can be utilized in the simulation of multidimensional field-media interactions. Recently, substantial efforts have been focused on the refinement of vector Preisach-type models of hysteresis. The purpose of this paper is to present a computationally efficient vector Preisach-type hysteresis model constructed from only two scalar models having orthogonally inter-related elementary operators. Such a model is implemented via a linear neural network (LNN) fed from the outputs of discrete Hopfield neural network (DHNN) blocks having step activation functions. With this DHNN-LNN configuration, it is possible to carry out the identification process using well-established widely available algorithms. Details of the model, its identification, and experimental testing are presented.     

Synthesis of resource conservation network with sink–source interaction

This article presents a mathematical model for the synthesis of resource conservation networks with interception placement. A comprehensive superstructure that incorporates all possible network configurations is used to facilitate the formulation. The synthesis task involves the allocation and interception of process sources to satisfy process sinks and environmental constraints. In particular, the interaction between the sinks and sources is addressed as the subject of the present study. Two literature case studies are solved to illustrate the proposed approach.     

An Artificial Intelligence Approach for Solving Stochastic Transportation Problems

Recent years witness a great deal of interest in artificial intelligence (AI) tools in the area of optimization. AI has developed a large number of tools to solve the most difficult search-and-optimization problems in computer science and operations research. Indeed, metaheuristic-based algorithms are a sub-field of AI. This study presents the use of the metaheuristic algorithm, that is, water cycle algorithm (WCA), in the transportation problem. A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution. Since the parameters are stochastic, the corresponding constraints are probabilistic. They are converted into deterministic constraints using the stochastic programming approach. In this study, we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization problems. WCA is influenced by the water cycle process of how streams and rivers flow toward the sea (optimal solution). WCA is applied to the stochastic transportation problem, and obtained results are compared with that of the new metaheuristic optimization algorithm, namely the neural network algorithm which is inspired by the biological nervous system. It is concluded that WCA presents better results when compared with the neural network algorithm.     

A systems-integration approach to the optimization of macroscopic water desalination and distribution networks: a general framework applied to Qatar’s water resources

The objective of this article is to introduce an optimization-based approach for the integrated design and operation of macroscopic water networks. A structural representation approach is developed to embed all potential configurations of interest. This representation accounts for water resources, desalination plants, water users, wastewater treatment facilities, and storage. Water recycle/reuse is enhanced via the use of treated water. Water utilization is improved by minimizing the losses of discharged water resulting from the linkage of power plants and thermal desalination plants and the lack of integration between water production and consumption. Excess water is saved in storage systems or injected in aquifers for strategic (long-term) storage. The developed approach also accounts for the economic values of water uses and storage and for the cost of water production and allocation. An optimization formulation is developed and solved to determine the optimal operation of the infrastructure. The solution also determines the optimal monthly allocation and storage of water resources. A case study is solved for managing the water resources in the State of Qatar while accounting for desalination, distribution, and storage. The solution indicates that storage in tanks reaches its maximum capacity in less than a month while storage in aquifers continues throughout the year as a strategic step towards water security. The solution also illustrates the need to treat wastewater in addition to using desalination of seawater. The output water streams with different qualities are assigned to proper destinations.     

An algebraic approach to targeting waste discharge and impure fresh usage via material recycle/reuse networks

This paper introduces an algebraic procedure that targets material-recycle networks. The problem involves the allocation of process streams and fresh sources to process units (sinks) with the objective of minimizing fresh usage and waste discharge. First, observations from the graphical targeting approach are transformed into algebraic insights. Then, a geometrical transformation is developed to account for the possibility of using impure fresh resources. These insights and geometrical transformations are arranged through a cascade analysis, which identifies and adjusts any recycle infeasibilities so as to maximize the recycle opportunities. A systematic non-iterative algebraic approach is developed to identify rigorous targets for minimum usage of impure fresh resources, maximum recycle of process resources and minimum discharge of waste. These targets are identified a priori and without commitment to the detailed design of the recycle/reuse network. The approach is valid for both pure and impure fresh resources. The devised procedure also identifies the location of the material-recycle pinch point and addresses its significance in managing process sources, fresh usage, and waste discharge. Two case studies are solved to illustrate the ease, rigor, and applicability of the developed targeting technique and its relationship to graphical targeting techniques.     

Effect of multi-stream heat exchanger on performance of natural gas liquefaction with mixed refrigerant

A thermodynamic study is carried out to investigate the effect of multi-stream heat exchanger on the performance of natural gas (NG) liquefaction with mixed refrigerant (MR). A cold stream (low-pressure MR) is in thermal contact with opposite flow of two hot streams (high-pressure MR and NG feed) at the same time. In typical process simulation with commercial software (such as Aspen HYSYS®), the liquefaction performance is estimated with a method of minimum temperature approach, simply assuming that two hot streams have the same temperature. In this study, local energy balance equations are rigorously solved with temperature-dependent properties of MR and NG feed, and are linked to the thermodynamic cycle analysis. The figure of merit (FOM) is quantitatively examined in terms of UA (the product of overall heat transfer coefficient and heat exchange area) between respective streams. In a single-stage MR process, it is concluded that the temperature profile from HYSYS is difficult to realize in practice, and the FOM value from HYSYS is an over-estimate, but can be closely achieved with a proper heat-exchanger design. It is also demonstrated that there exists a unique optimal ratio in three UA’s, and no direct heat exchanger between hot streams is recommended.     

Energetische Betrachtung von Schraubprozessen am Beispiel Impulsschraubtechnik – mögliche Kenngrößen zur erweiterten und werkzeugunabhängigen Beurteilung der Qualität von Schraubenverbindungen**

For the assembly of safety relevant tightening joints mainly continuous rotating tools are used. The operating principle of controlled impulse tightening tools can be an useful extension. In practice, the use of pulsating tools turns out to be difficult due to missing or complicated procedures and guidelines for traceable calibration. Further the evaluation of the maximum torque of a pulse results in large fluctuations regarding the switch-off accuracy of these tools. In this context, the energetic approach for tightening joints creates the potential for a tool-independent assessment of the assembly quality. Based on analyzes of ongoing energetic conversions from the entry of the tool powertrain to the tightening preload, the target parameter, the quantity of energy can be consulted for a process evaluation. First requirement is a comprehensive theoretical understanding of the processes while the pulsating energy input. This has to be verified through practical investigations in the next step. The first aim is to take account of the entire progress of an impulse curve in evaluation. As well the comparability of different tool types should be enabled with this approach. Furthermore, the use of controlled impulse tightening tools should become simpler with the “strategies” of performance limitation and oversizing.     

“Selfish” algorithm for reducing the computational cost of the network survivability analysis

In Nature, the primary goal of any network is to survive. This is less obvious for engineering networks (electric power, gas, water, transportation systems, etc.) that are expected to operate under normal conditions most of the time. As a result, the ability of a network to withstand massive sudden damage caused by adverse events (or survivability) has not been among traditional goals in systems design. Reality, however, calls for the adjustment of design priorities. As modern networks develop toward increasing their size, complexity, and integration, the likelihood of adverse events also increases due to technological development, climate change, and activities in the political arena, among other factors. Under such circumstances, a network failure has an unprecedented effect on lives and economy. To mitigate the impact of adverse events on network operability, the survivability analysis must be conducted at an early stage in network design. Such analysis requires the development of new analytical and computational tools. A computational analysis of network survivability is an exponential time problem that makes the analysis computationally unfeasible for large-scale networks. The current paper describes a new algorithm in which reduction of the computational cost is achieved by mapping an initial network topology with multiple sources and sinks onto a set of simpler smaller topologies with multiple sources and a single sink. Steps for further reducing the time and space expenses of computations are also discussed.     

Modelling for agile manufacturing systems

To address the challenges of a rapidly changing manufacturing market, a new type of manufacturing system with characteristics of reconfigurability, reusability and scalability, an agile manufacturing system (AMS) has to be developed. Reconfigurability is an essential feature of AMS. Such a system can use basic building blocks, both hardware and software, which can be reconfigured quickly and reliably. A fundamental early step in the reconfiguring process for an agile manufacturing system is to develop a model that adequately describes the proposed system, in order to be able to study and evaluate the impact of the reconfiguring decision on the system performance, before its construction. Therefore, the rapid modelling and reusable modelling capabilities are demanded. In this paper, an Object & Knowledge-based Interval Timed Petri-Net (OKITPN) approach is proposed, which provides an object-oriented and modular method of modelling manufacturing activities. It includes knowledge, interval time, modular and communication attributes. The features of object-oriented modelling allow the AMS to be modelled with the properties of classes and objects, and make the concept of software IC possible for rapid modelling of complex AMSs. Once all of the Interval Timed Petri-Net (ITPN) objects are well defined the developers need to consider only the interfaces and operations relating to the ITPN objects. In order to demonstrate the capability of the proposed OKITPN, it has been used to model rapidly AMSs that are reconfigured according to requirements.     

Inferring social network structure in ecological systems from spatio-temporal data streams

We propose a methodology for extracting social network structure from spatio-temporal datasets that describe timestamped occurrences of individuals. Our approach identifies temporal regions of dense agent activity and links are drawn between individuals based on their co-occurrences across these ‘gathering events’. The statistical significance of these connections is then tested against an appropriate null model. Such a framework allows us to exploit the wealth of analytical and computational tools of network analysis in settings where the underlying connectivity pattern between interacting agents (commonly termed the adjacency matrix) is not given a priori. We perform experiments on two large-scale datasets (greater than 10⁶ points) of great tit Parus major wild bird foraging records and illustrate the use of this approach by examining the temporal dynamics of pairing behaviour, a process that was previously very hard to observe. We show that established pair bonds are maintained continuously, whereas new pair bonds form at variable times before breeding, but are characterized by a rapid development of network proximity. The method proposed here is general, and can be applied to any system with information about the temporal co-occurrence of interacting agents.     

Ein Netzwerk von Kontrolltechnologien

A Network of Control Technologies. A New Perspective on the Origins of Modern Surgery This essay describes the emergence of modern surgery as the construction of a network of control technologies. The theoretical basis of this analysis makes use of Actor Network Theory and Joseph Rouse’s Foucaultian approach for characterizing the laboratory as an artificial micro-world. On a concrete level, the paper first deals with the history of surgical instruments as tools for a controlled intervention into the human body. The introduction of antisepsis and asepsis make up a second example, since these technologies embody with particular clarity the increase of control that went along with the emergence of modern surgery. These examples demonstrate the use of the concept of control as an analytic category for a better understanding of the origins of modern surgery’s technological success and its interpretation in the context of the emergence of modern societies.