Predictive stochastic analysis of massive filter-based electrochemical reaction networks

Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are not well understood and products are unknown. Here we report new methods of CRN generation and analysis that overcome these limitations. By constructing CRNs using filters rather than templates, we can capture species and reactions that are unintuitive but fundamentally reasonable. The resulting massive CRNs can then be interrogated via stochastic methods, revealing thermodynamically bounded reaction pathways to species of interest and automatically identifying network products. We apply this methodology to study solid-electrolyte interphase (SEI) formation in Li-ion batteries, generating a CRN with ~86,000,000 reactions. Our methods automatically recover SEI products from the literature and predict previously unknown species. We validate their formation mechanisms using first-principles calculations, discovering novel kinetically accessible molecules. This methodology enables the de novo exploration of vast chemical spaces, with the potential for diverse applications across thermochemistry, electrochemistry, and photochemistry.

Predictive Maintenance in the Automotive Sector: A Literature Review

With the rapid advancement of sensor and network technology, there has been a notable increase in the availability of condition-monitoring data such as vibration, temperature, pressure, voltage, and other electrical and mechanical parameters. With the introduction of big data, it is possible to prevent potential failures and estimate the remaining useful life of the equipment by developing advanced mathematical models and artificial intelligence (AI) techniques. These approaches allow taking maintenance actions quickly and appropriately. In this scenario, this paper presents a systematic literature review of statistical inference approaches, stochastic methods, and AI techniques for predictive maintenance in the automotive sector. It provides a summary on these approaches, their main results, challenges, and opportunities, and it supports new research works for vehicle predictive maintenance.

Template-free reaction networks enable predictive and automated analysis of complex electrochemical reaction cascades

Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are unintuitive, and products are unknown. Here we report new methods of CRN generation and analysis that overcome these limitations. By constructing CRNs using filters rather than templates, we can capture species and reactions that are unintuitive but fundamentally reasonable. The resulting massive CRNs can then be interrogated via stochastic methods, revealing thermodynamically bounded reaction pathways to species of interest and automatically identifying network products. We apply this methodology to study solid-electrolyte interphase (SEI) formation in Li-ion batteries, generating a CRN with ~86,000,000 reactions. Our methods automatically recover SEI products from the literature and predict previously unknown species. We validate their formation mechanisms using first-principles calculations, discovering multiple novel kinetically accessible molecules. This methodology enables the de novo exploration of vast chemical spaces, with the potential for diverse applications across thermochemistry, electrochemistry, and photochemistry.

Reservoir Management Optimization Model Employing Combination Deterministic and Probabilistic Approach for Carbon Sequestration Storage and Utilization: A Case Study from East Natuna

East Natuna is well known for its huge natural gas reserves with a very high CO2 content. The appearance of CO2 content in an oil and gas field is always considered as waste material and will severely affect the economic value of the field. The higher the content, the more costly the process, both technically and environmentally. In this research, the newly proposed reservoir management approach called CSSU (Carbon Sequestration Storage and Utilization) method is trying to change the paradigm of CO2 from waste material into economic materials. The novelty of this research is the combined optimization of deterministic and stochastic methods with the Particle Swarm Optimization (PSO) algorithm to answer complex and non-linear problems in the CSSU (Carbon Sequestration Storage and Utilization) method. The CSSU method is an integration of geological, geophysical, reservoir engineering and engineering economics with the determination of technical and economic optimization of the use of CO2 produced as working fluid in a power generation system that has been conditioned through an injection-production system in geological formations. The CSSU research area is located in a sedimentary basin that has a giant gas field with 70% CO2 content. The Volumetric Storage Capacity for CO2 injection process in research area is 1,749.14 BCF or 94.01 MMTon which being calculated based on static modeling considering geological, geophysical and petrophysical aspects. A combination of Compositional, Geomechanics and Thermal reservoir simulation model had been conducted to determines the Storage Injection capacity and later to prove the CSSU method in which CO2 fluids will be utilized as working fluid, 1 case was built using 2 Injection Wells and 1 CO2 fluid Production Well. The simulation results show with 1 production well the total of CO2 fluid injected from 2 Injection wells can almost double the injection total capacity up to 1,150 BCF. The utilization of supercritical CO2 fluid as working fluid can produce 55 – 133.5 MMBTU/Day or 0.67 - 1.63 MW from 1 production well for 25 years timeframe. The CSSU method is optimized by deterministic and stochastic methods using the Particle Swarm Optimization (PSO) algorithm by looking the technical and economical aspects. The technical optimization aspect is being analyzed by electricity production versus well counts. The economical optimization is being analyzed by operational expenditure saving versus well counts and electricity produced versus NPV 10%. From both aspects the 4 injector wells case and NPV 200.00 MM US$ gives the most optimum result within technically and economically. The CSSU economic model proved with CSSU scheme the economical value is being increased by 57 MMUS$ after operating cost efficiency due to the electricity savings, 92 MMUS$ due to Carbon Trading which resulting the NPV 10% is 172.77 MMUS$.

Rigorous Performance Evaluation of Stochastic Optimization for Water Injection Strategies

This paper presents an approach to optimize field water injection strategies using stochastic methods under uncertainty. For many fields, voidage replacement was the dictating factor of setting injection strategies. Determining the optimum injection-production ratio (IPR) requires extensive experience taking into consideration all the operational facility constraints. We present the outcome of a study, in which several optimization techniques were used to find the optimum field IPR values and then elaborate on the techniques? strengths and weaknesses. The synthetic reservoir simulation model, with millions of grid blocks and significant numbers of producers and injectors, was divided into seven IPR regions based on a streamline study. Each region was assigned an IPR value with an associated uncertainty interval. An ensemble of fifty probabilistic scenarios was generated by experimental design, using Latin Hypercube sampling of IPR values within tolerance limits. Scenarios were used as the main sampling domain to evaluate a family of optimization engines: population-based methods of artificial intelligence (AI), such as Genetic algorithms and Evolutionary strategies, Bayesian inference using sequential or Markov chain Monte Carlo, and proxy-based optimization. The optimizers were evaluated based on the recommended IPR values that meet the objective of minimizing the water cut by maximizing oil production and minimizing water production. The speed of convergence of the optimization process was also a subject of evaluation. To ensure unbiased sampling of IPR values and to prevent oversampling of boundary extremes, a uniform triangular distribution was designed. The results of the study show a clear improvement of the objective function, compared to the initial sampled cases. As a direct search method, the Evolutionary strategies with covariance matrix adaptation (ES-CMA) yielded the optimum IPR value per region. While examining the effect of applying these IPR values in the reservoir simulation model, a significant reduction of water production from the initial cases without an impact on the oil production was observed. Compared to ESCMA, other optimization methods have dem

Strain gauge placement optimization methodology to measure multiaxial loads of complex structure

The use of a strain gauge to measure loads is, in some respects, similar to its use in determining stress, but a different approach is required. In load measurement, it is necessary to compile a suitably selected configuration of strain gauges, which can be used to measure often very complex loads of the structure. For designing the engine mount instrumentation for the Flying Test Bed, an optimization tool has been developed. The algorithm and the theory behind the instrumentation design are described in detail. The basic principle is to find the strain gauge configuration that eliminates the measurement error due to the noise in the measured signal as much as possible. The input for optimization is the strain response of the structure to the applied loads analyzed using the FE model. In contrast to the common strategy using purely stochastic methods, this developed tool uses a hybrid approach based on a combination of a heuristic approach with repeated deterministic local optimization. The optimization is focused on the connection of a simple uni-axial strain gauge to a quarter-bridge and a T-rosette to a half-bridge that provides temperature compensation. Furthermore, an approach is proposed that takes into account the possibility of failure of some strain gauges. The instrumentation is thus robust and allows to obtain quality data even in the event of failure of some of the strain gauges.

Breakdown of a Nonlinear Stochastic Nipah Virus Epidemic Models through Efficient Numerical Methods

Nipah virus (NiV) is a zoonotic virus (transmitted from animals to humans), which can also be transmitted through contaminated food or directly between people. According to a World Health Organization (WHO) report, the transmission of Nipah virus infection varies from animals to humans or humans to humans. The case fatality rate is estimated at 40% to 75%. The most infected regions include Cambodia, Ghana, Indonesia, Madagascar, the Philippines, and Thailand. The Nipah virus model is categorized into four parts: susceptible (S), exposed (E), infected (I), and recovered (R). Methods: The structural properties such as dynamical consistency, positivity, and boundedness are the considerable requirements of models in these fields. However, existing numerical methods like Euler–Maruyama and Stochastic Runge–Kutta fail to explain the main features of the biological problems. Results: The proposed stochastic non-standard finite difference (NSFD) employs standard and non-standard approaches in the numerical solution of the model, with positivity and boundedness as the characteristic determinants for efficiency and low-cost approximations. While the results from the existing standard stochastic methods converge conditionally or diverge in the long run, the solution by the stochastic NSFD method is stable and convergent over all time steps. Conclusions: The stochastic NSFD is an efficient, cost-effective method that accommodates all the desired feasible properties.

Investment Planning in Oil Production Enhancement Projects in Ukraine Based on Joint Usage of 3D Modelling and SPE-PRMS Reserves Classification

Most of the discovered oil fields in Ukraine entering a declining production stage. Many of these assets have good potential for production increasing and require investments. The risks of such investments are related to the uncertainty of geological information, production data, and the total amount of reserves and resources. This paper describes the study of the joint use of 3D hydrodynamic modeling and reserves estimation according to the SPE-PRMS classification, which together allowed to assess and significantly reduce investment risks for oil production enhancement projects. The use of 3D modeling is one of the key elements during field exploration and production, because of coordination of all available geological and field data it is often possible to discover new, previously unknown features of the geological structure and identify high potential areas to increase production. In this paper petrophysical, geological and hydrodynamic modeling tools and material balance method have been used to consolidate existing geological and field data and create 3D model of the field in Western oil and gas bearing region of Ukraine. Also, for uncertainty analysis of the initial hydrocarbons in-place and IOR project investment presentation the SPE-PRMS classification was used. Comprehensive usage of material balance tools, field development history analysis, well performance changes, and fluid properties behavior revealed inconsistencies in the geological data and hypothesized the existence of a gas cap in the oil deposit and identify a faults system through the reservoir. After well logging these hypotheses has been confirmed, which allowed achieving a good history match of the model for the entire field and each well. Based on the matched model, a comprehensive field development strategy was proposed, which also considered all existing limitations related to production and infrastructure issues. The best scenario of field development was selected, according to the results of the economic assessment in terms of investment attractiveness. Based on the created 3D geological model, hydrocarbons reserves and resources were estimated using deterministic and stochastic methods and have been classified according to the SPE-PRMS. Reserves categories were assessed by the degree of commercial maturity of the project based on ten possible field development scenarios and high potential zones for infill drilling, plays exploration, and IOR project implementation was selected. The integrated approach to the field development strategy assessment and the input data uncertainties allowed to consider all available geological information and field data to create a comprehensive pilot investment IOR project. The proposed approach allows to solve complex problems of potential investments risks assessment and reduction in IOR projects and discover new assets' potential on the example of a complex field in the inner zone of the Pre-Carpathian Depression.