Artificial intelligence for optimization: Unleashing the potential of parameter generation, model formulation, and solution methods

This paper describes the development of a fully implicit general thermal model (ISCOM) for simulating in situ combustion or steam processes. The model includes four phases, a variable number of oil components, a variable number of chemical reactions, gravity and capillary pressure terms, and the possibility of modeling emulsions. The finite-difference formulation of ISCOM operates in one, two or three dimensions and contains implicit and sequential implicit solution techniques as options. It is highly stable, averaging 30% saturation or molar fraction changes during each time step. The model includes the use of pseudo-phase equilibrium ratios which allows a single formulation, even though phases may disappear. The computer program was designed to emphasize clarity and to program was designed to emphasize clarity and to optimize storage. The matrix solution technique is a modified form of Gaussian elimination. The variables and fundamental equations have been aligned so that pivoting for numerical stability is not necessary. pivoting for numerical stability is not necessary. Other special features include Newtonian iteration with numerically calculated derivatives, embedding of the coke equation, two-point upstream and harmonic mobilities as options to reduce truncation error and the grid orientation effect, and an automatic time step selector. Results are presented showing the sensitivity to changes in number of grid blocks and time step size and showing the influence of harmonic weighting on the grid orientation effect. Introduction Thermal methods occupy a prominent position in the recovery of low API crudes. The processes taking place during thermal oil recovery are of a complex place during thermal oil recovery are of a complex nature. Experience has shown that in order to model such processes, any numerical simulator must be extremely stable to produce practical results. The trend, therefore, has been towards increasing degrees of implicitness in the construction of such simulators. This demands, however, the utilization of large computer storage and run times, especially for multi-dimensional problems. This paper describes the development of a fully implicit, finite-difference, general purpose thermal simulator (ISCOM) for modeling in situ combustion and steam processes. The storage and computation time problems have been resolved by the development of a problems have been resolved by the development of a special matrix hand reducing option. Another feature of thermal recovery simulations in heavy oils is the severity of the grid orientation effect. In ISCOM this effect is alleviated by an option which introduces harmonic mobility weighting. The steam drive/soak options are subsets of the in situ combustion model, whereby unnecessary reactions and equations are removed. The steam versions are described here only briefly. Therefore this paper concentrates primarily on the in situ combustion option. The formulation of the model, physical property package, and method of solution will be described, package, and method of solution will be described, together with the results of some test calculations. FORMULATION OF THE MODEL Basic Assumptions The mathematical model is based on the following assumptions:The model can operate in one, two or three dimensions with variable grid spacing.The number of components is variable, and components are distributed among four phases: water (w), oil (o), gas (g), and solid (c).Water and oil components may be present in both oil and water phases, allowing for an approximate treatment of emulsions.The number and type of chemical reactions is variable.

Offshore wind farm installation requires complex planning under uncertainty, and numerous system models have been developed to support scheduling and resource allocation. However, differences in model formulation, solution methods, and level of detail make it difficult to identify the best approaches for a given project. This paper develops a domain-specific taxonomy to compare and select system models for offshore wind installation planning. The taxonomy is grounded in a systematic literature review of 194 studies and adapts general model comparison criteria to four dimensions: application context, model formulation, solution methods, and model features. The taxonomy is applied to six representative offshore scheduling approaches to illustrate its use. Results show that the taxonomy clarifies model scope, strengths, limitations, and trade-offs, providing a structured basis for model selection and revealing opportunities for methodological refinement. While tailored to offshore wind installations, the approach could be transferable to other domains with heterogeneous system models.

This paper describes a three-dimensional, highly implicit numerical model for simulating steamflooding with distillation or solution gas. The model uses direct solution to solve simultaneously three and four equations for the dead oil and two-component oil cases, respectively. The model is compared in stability and computing time with a model reported earlier. The paper includes comparative discussion of alternate steamflood model formulations, one of which we have adopted as a highly stable, isothermal, black-oil model formulation. Introduction A brief review of published descriptions of steamflood models is given in an earlier paper. That paper described a partially compositional, three-dimensional model that solves first a single-variable pressure equation, then two simultaneous saturation equations. In our experience with dead-oil steamflood problems, that model exhibits adequate stability in most cases and marginal stability in some cases. In some compositional problems, the formulation of that model leads to problems, the formulation of that model leads to deteriorating material balances for light hydrocarbon components. The model described here was developed to gain improved stability for all types of steamflood problems and to eliminate the material balance problems and to eliminate the material balance shortcoming of the earlier model formulation in compositional problems. This highly implicit, three-dimensional model treats oil as a two-component mixture to accommodate problems involving solution or inert gas or distillation. The model simultaneously solves three equations for the dead-oil case and four equations for the compositional case. Transmissibilities, capillary pressures, and production terms are treated pressures, and production terms are treated implicitly in saturations and composition; they also are treated implicitly in temperature in grid blocks where no free gas is present. The term "implicit" refers to evaluation of interblock flow terms and production rates at the new time level, n + 1. We have found insensitivity to explicit or implicit dating of molar densities and viscosities in these terms and therefore simply evaluate them explicitly. We evaluate relative permeabilities at time level n + 1 by the first-order permeabilities at time level n + 1 by the first-order approximation, which ignores second- and higher-order Taylor series terms . Temperature dependence of relative permeability, if present, is treated explicitly. We present the model equations, and describe the highly implicit formulation and method for solution. This model is compared with the earlier steamflood model in stability and efficiency through discussion and example field problems. MODEL DESCRIPTION BASIC EQUATIONS The model consists of five equations expressing conservation of energy, conservation of mass, and phase equilibrium. The mass conservation equations phase equilibrium. The mass conservation equations apply to water and to two hydrocarbon components. In finite-difference form, these equations are Energy Balance (1) Mass Balance on H2O (2) SPEJ P. 369

Urban design has been valuable in bringing the principles of transit-oriented development (TOD) into reality. However, a majority of recommendations summarized by scholars for promoting TODs through urban design have failed to promote the progress of the urban design. The main reason for this issue is the long-standing tradition of design decision-making based on designers’ experience and the lack of quantitative assessment feedback on design schemes. With the development of big data and artificial intelligence, optimisation-based generative design has been explored to overcome the limitations of experience-based urban design approaches. However, the techniques and workflows are still not mature enough for designers to adopt. In response to these challenges, this study proposes a framework that integrates the generative design method and data-driven decision-making approach for urban design solutions that better implement the basic principles of TODs. Based on the urban design intelligence for TODs, this framework uses parametric tools and models to evaluate the generative urban design proposals, providing timely feedback to support the design decisions. The framework is applied to a case study to examine the feasibility. It is demonstrated that this approach succeeds in selecting optimal TOD design solutions. The role of designers’ decision-making in generative urban design, as well as the importance of quantitative and qualitative assessment in experience-based decision-making, are highlighted.

"Management Science/Operations Research. Model Formulation and Solution Methods." Journal of the Operational Research Society, 29(4), pp. 389–390

This paper describes the WITCH - World Induced Technical Change Hybrid - model in its structure, calibration, and the implementation of the SSP/RCP scenario implementation. The WITCH model is a regionally disaggregated hard-linked model based on a Ramsey type optimal growth model and a detailed bottom-up energy sector model. A particular focus of the model is the modeling or technical change and RnD investments and the analysis of cooperative and non-cooperative climate policies. Moreover, the WITCH 2016 version now includes land-use change modeling based on the GLOBIOM model, and air pollutants, as well as detailed modeling of the transport sector and the possibility for stochastic modeling. This version has been also used to implement the Shared Socioeconomic Pathways (SSPs) set of scenarios and RCP based climate policies to provide a new set of climate scenarios. In this paper, we describe in detail the mathematical formulation of the WITCH model, the solution method and calibration, as well as the implementation of the five SSP scenarios. This report therefore provides detailed information for interested users of the model, and for understanding the implementation of the different “worlds of the SSP.

To predict dispersion curves it is common to use different solution approaches depending on the material type, isotropic or composite, of the medium in which the wave propagates. The two different solution methods are defined in different domains, frequency–wavespeed domain for isotropic materials, and wavenumber–wavespeed domain for composites which can lead to difficulties, and unsatisfying results when predicting the dispersion curves for hybrid laminates which contain both isotropic and composite materials. This article, therefore, proposes a unified formulation defined in the wavenumber–wavespeed domain for both isotropic and composite materials. The unified formulation, simple, and mathematically straightforward formulation, utilizes Christoffel’s equation for a lamina to obtain the eigenvalues and eigenvectors. The eigenvalues and eigenvectors are then used to set up the field matrix from which the dispersion curves could be retrieved. Once the dispersion curves were obtained the waves are grouped using a modeshape analysis. A spline algorithm is applied to obtain a continuous solution from a rough domain which was used to reduce computational time. In addition, this article highlights the challenges faced in the numerical process, and provides a discussions of the methods used to overcome these obstacles.

An implicit finite difference method with implicit boundary conditions is employed to solve the steady Euler's equations for flows past arbitrary geometries. The resulting code is used to investigate in a systematic way various aspects of flow past airfoils at transonic speeds such as, method of solution, boundary conditions, grid stretching and generation, shock and sonic point operators, the Kutta condition, and smoothing. Results obtained are in good agreement with results of other codes. Moreover, it appears that the method of solution employed is such that Kutta's condition need not be invoked. This statement appears to be valid for other existing schemes employed in the solution of Euler's equations.

This work improves a condition monitoring approach for wind turbine main bearings based on data from the supervisory control and data acquisition system, and on the principle of energy conservation. Previous works have proposed a main bearing temperature parametric model which residue in respect to measured data was used to detect main bearing degradation. Such an approach allowed detections with anticipation of the failure of around one month for the analyzed case studies, showing therefore a good potential for industrial applications. The present work investigates a relaxed formulation of the parametric model and introduces a novel detection criterion based on the model coefficients. This new formulation is evaluated within an operating wind farm, showing improved detection capabilities, and longer anticipation of failures.

A generator of dissipative methods for the numerical solution of the Schrödinger equation

To propose an alternative method of thermoacoustic wave generation based on heating of magnetic nanoparticles (MNPs) using alternating magnetic field (AMF). The feasibility of thermoacoustic wave generation from MNPs by applying a short-burst of AMF or a frequency-modulated AMF is theoretically analyzed. As the relaxation of MNPs is strongly dependent upon the amplitude and frequency of AMF, either an amplitude modulated, fixed frequency AMF (termed time-domain AMF) or a frequency modulated, constant amplitude AMF (termed frequency-domain AMF) will result in time-varying heat dissipation from MNPs, which has the potential to generate thermoacoustic waves. Following Rosensweig's model of specific power loss of MNPs in a steady-state AMF, the time-resolved heat dissipations of MNPs of superparamagnetic size when exposed to a short bursting of AMF and∕or to a linearly frequency chirped AMF are derived, and the resulted acoustic propagation is presented. Based on experimentally measured temperature-rise characteristics of a superparamagnetic iron-oxide nanoparticle (SPION) matrix in a steady-state AMF of various frequencies, the heat dissipations of the SPION under time-domain and frequency-domain AMF configurations that could have practical utility for thermoacoustic wave generation are estimated. The initial rates of the temperature-rise of the SPION matrix were measured at an iron-weight concentration of 0.8 mg∕ml and an AMF frequency of 88.8 kHz to 1.105 MHz. The measured initial rates of temperature-rise were modeled by Rosensweig's theory, and projected to 10 MHz AMF frequency, at which a 1 μs bursting corresponding to a 1.55 mm axial resolution of acoustic detection could contain 10 complete cycles of AMF oscillation and the power dissipation is approximately 84 times of that at 1 MHz. Exposing the SPION matrix to a 1 μs bursting of AMF at 10 MHz frequency and 100 Oe field intensity would produce a volumetric heat dissipation of 7.7 μJ∕cm(3) over the microsecond duration of the AMF burst. If the SPION matrix is exposed to a 1 ms long AMF train at 100 Oe field intensity that chirps linearly from 1 to 10 MHz, the volumetric heat dissipation produced over each 2π phase change of the AMF oscillation is estimated to increase from 0.15 to 1.1 μJ∕cm(3) within the millisecond duration of the chirping of AMF. The heat dissipations upon SPION (∼1 mg∕ml iron-weight concentration) by a 1 μs bursting of 100 Oe AMF at 10 MHz and a 1 ms train of 100 Oe AMF that chirps linearly from 1 to 10 MHz were estimated to determine the potential of thermal-acoustic wave generation. Although thermoacoustic wave generation from MNPs by time- or frequency-domain AMF applications is predicted, the experimental generation of such a wave remains challenging.

In this study, a comparison of two artificial intelligence inspired solution methods employed to solve Security Constrained Economic Dispatch (SCED) of Ethiopian Renewable Energy Systems (ERES) is presented. The solution methods are Efficient & Parallel Genetic Algorithm (EPGA) and Hopfield Neural Network (HNN). This paper argues that employing intelligent SCED that considers power mismatch and intermittency of renewables can solve ERES’s recursive blackouts. A simulation was conducted on MATLAB. According to the results, both solution methods provide the best solutions for their respective purposes. For providing accurate forecast & predictive control of intermittent generation, it is imperative to employ HNN. When obtaining global maxima of multi-objective function is required, it is recommended to employ EPGA. Generally, employing intelligent SCED is a key planning step in adopting smarter grids as it reduces the production cost and the number of blackouts while increasing the security level of ERES.KeywordsHopfield neural networksGenetic algorithmsSecurity constraintsEconomic dispatchRenewable energy systemsEthiopian power grid

General stochastic ridesharing user equilibrium problem with elastic demand

The overall objective of this study is to analyze the contribution of artificial intelligence to optimizing intelligence analysis and decision-making within the scope of the Public Prosecutor's Office. The specific objectives include examining the history and evolution of intelligence activities, as well as describing the types of intelligence analysis applied in the Public Prosecutor's Office. The study aims to highlight fundamental aspects that guide Intelligence Analysis, especially in the context of the Office of the Attorney General, using AI. The methodology used involved a literature review conducted through reliable sources such as SciELO, Google Scholar, and the Public Prosecutor's Office websites. Publications from the period of 2000 to 2024, in Portuguese, Spanish, and English, were selected. Selective filters, such as "Intelligence Analysis," "Intelligence Activity," and "Artificial Intelligence in the Public Prosecutor's Office," were applied to ensure the relevance of the results. Duplicate articles and those not aligned with the study's objective were excluded. Data extraction prioritized relevance concerning the title and objective. The selected works were initially analyzed by titles and abstracts, and subsequently through full-text readings. The definition of intelligence can vary according to the context and perceived threats by those defining it, but it always maintains a fundamental relationship with information. Intelligence activity has been essential since the dawn of civilizations when it was recognized that knowledge is power, a concept that remains relevant. Thus, with the advancement of technology, especially Artificial Intelligence, the processes and operations of individuals and organizations have completely changed. In Brazil, the Brazilian Intelligence System, with roots in military and foreign relations antecedents, began to organize institutionally in 1927. One of the biggest challenges is the lack of a system that promotes cooperation among the various involved agencies, highlighting the importance of intelligence activities within the Public Prosecutor's Office (MP). This body, with multiple functions, acts in both the investigative and judicial phases, ensuring the legal compliance of actions by other investigative bodies. Artificial Intelligence (AI) can increase the speed of processes and efficiency in the public sector, offering new methods of solutions. AI uses algorithms that allow machines to perform repetitive tasks in a standardized way, previously carried out by humans. Classified as strategic, tactical, and operational, each type of intelligence has a specific role in planning and executing actions. This study highlights the importance of intelligence today, especially in the MP, with AI revolutionizing the execution of repetitive tasks, promoting greater accuracy and reliability.

More than ten billion tons of construction waste are generated every year in the world. The large volume of construction waste not only increases costs for contractors, but also poses a threat to the environment. A significant proportion of construction waste consists of off-cuts of raw materials. Therefore, to reduce construction waste, this study builds an optimization model to reduce the volume of off-cuts of raw materials. We then develop two solution methods—a mixed-integer linear programming method and a column generation method—to solve the proposed optimization model. We conduct numerical experiments to test the efficiency and applicability of our proposed model. The mixed-integer linear programming method obtains optimal solutions and is suitable for solving small-scale instances, whereas the column generation method gives high-quality solutions within seconds and is suitable for solving large-scale instances. In the large-scale instances, the column generation method reduces waste by over 10% compared to the use of two straightforward decisions rules. Our findings will help construction projects decrease material off-cuts, reduce costs, and achieve sustainable construction.