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Production optimization is seen as a key technology for adjusting the flow direction of water-driven oil and improving the development effect of oil fields. However, existing optimization methods are often found to struggle in drawing from historical control experience, resulting in suboptimal efficiency. A reinforcement learning reservoir production optimization method based on historical control experience was proposed. The method, based on the soft actor-critic algorithm, was first modeled as a Markov decision process for production optimization. Within this decision process, the pressure and saturation fields of the reservoir were taken as observable states for the reinforcement learning agent, while the economic net present value of development schemes was treated as rewards. Subsequently, reservoir states were mapped to production control schemes by the reinforcement learning agent, which continually interacted with the reservoir environment to accumulate historical control experience. Ultimately, by leveraging this experience, optimal policies were rapidly learned by the agent. The proposed method is applied to a reservoir block for testing. The experimental results show that the proposed reinforcement learning reservoir production optimization method based on historical control experience outperforms traditional evolutionary algorithms and surrogate model methods in terms of optimization performance and oil increase and water control. [ABSTRACT FROM AUTHOR]

The Gas-Oil Ratio (GOR) is a crucial production parameter in oil reservoirs. An increase in GOR results in higher gas production and lower oil production, potentially leading to well shut-ins due to economic infeasibility. This study focuses on a real fractured oil field that requires urgent production operations to reduce the producing GOR. In this study, the static model for the field was developed using commercial software, involving steps such as data collection, fault modeling, meshing, and statistical analysis to prepare for dynamic simulation. The dynamic model incorporates geometry, gridding, and rock properties from the static model, utilizing a dual-porosity approach for the naturally fractured reservoir and the Peng-Robinson equation for fluid phase behavior. Initial reservoir conditions, production history, and rock-fluid interactions were defined, with relative permeability curves indicating a water-wet reservoir and low critical gas saturation affecting the GOR. To better understand the relationship between reservoir and production parameters, a detailed sensitivity analysis was performed using the Response Surface Methodology (RSM). Following the sensitivity analysis, a history matching process was conducted using the Designed Exploration and Controlled Evolution (DECE) optimizer to validate the model for future forecasts. Six operational scenarios were defined to decrease the production GOR and enhance final recovery from the field. The results indicate that the water injection scenario is effective in preventing the GOR increase by maintaining reservoir pressure, thereby sustaining production over a longer period. This scenario also improves oil recovery by approximately 6% compared to the base case. Finally, optimization was carried out using the DECE optimizer for each scenario to fine-tune the operational parameters. The goal was to maximize oil revenue for each scenario during the optimization process. This study stands out as one of the few that provides a comprehensive analysis of production behavior and development planning for a real fractured reservoir with high producing GOR. [ABSTRACT FROM AUTHOR]

To further expand the variety of oat milk products and develop an oat plant-based yogurt (OPY) without animal ingredients, this study used whole oat flour as raw material to prepare oat milk through enzymatic hydrolysis, inoculated with commercial fermentation culture to prepare OPY. The production was optimized using single-factor experiments and response surface methods, and the properties of OPY was analyzed. The results showed that the optimal production parameters for OPY were as follows: solid-liquid ratio was 1:3.9 (g:mL), culture addition ratio was 0.17% (based on the addition ratio of whole oat flour), and fermentation time was 7 hours. Under these conditions, the sensory score of OPY was 86.33±0.58, the β-glucan content (dry basis) was 8.37±0.20 mg/100 mL. Compared to stirred yogurt (SY), the titratable acidity and fat content of OPY decreased by 32.1% and 15%, respectively. The water holding capacity and total solid content of OPY increased by 41% and 34.76%, respectively. The texture and rheological analysis showed that the cohesiveness (0.71±0.00), springiness (0.83±0.00), elastic modulus and viscosity modulus of OPY were significantly higher than that of SY. After enzymatic hydrolysis and fermentation, OPY retained β-glucan, and had better rheological properties. In addition, several physicochemical indicators and nutritional components of OPY were better than those of SY, indicating that the process optimization had been successful. This study has high reference value for the actual production of OPY, and also provides new ideas for the development of fermented oat beverages.

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study introduces a comprehensive framework designed to enhance production efficiency by integrating maintenance strategies, energy costs, and production specifications. This integration is achieved through a novel empirical method for estimating state–action costs, suitable for both machines with measurable and non-measurable states-of-health. We address the challenge of under-determination in state–action cost optimization by employing a k-means clustering approach, ensuring robustness and applicability. Utilizing an adapted SARSA algorithm, our framework optimally controls shop-floor machinery to minimize the global cost function. The efficacy of the state–action cost estimation method is validated using NASA’s C-MAPSS dataset. Additionally, the optimization strategy is further corroborated through its successful implementation in an autonomous mining cart model on the shop floor. Our results highlight the framework’s ability to optimize machine lifetime and production processes effectively, providing tailored solutions that adapt to varying operational conditions without depending on predefined machine degradation models and costs.

The Gas-Oil Ratio (GOR) is a crucial production parameter in oil reservoirs. An increase in GOR results in higher gas production and lower oil production, potentially leading to well shut-ins due to economic infeasibility. This study focuses on a real fractured oil field that requires urgent production operations to reduce the producing GOR. In this study, the static model for the field was developed using commercial software, involving steps such as data collection, fault modeling, meshing, and statistical analysis to prepare for dynamic simulation. The dynamic model incorporates geometry, gridding, and rock properties from the static model, utilizing a dual-porosity approach for the naturally fractured reservoir and the Peng-Robinson equation for fluid phase behavior. Initial reservoir conditions, production history, and rock-fluid interactions were defined, with relative permeability curves indicating a water-wet reservoir and low critical gas saturation affecting the GOR. To better understand the relationship between reservoir and production parameters, a detailed sensitivity analysis was performed using the Response Surface Methodology (RSM). Following the sensitivity analysis, a history matching process was conducted using the Designed Exploration and Controlled Evolution (DECE) optimizer to validate the model for future forecasts. Six operational scenarios were defined to decrease the production GOR and enhance final recovery from the field. The results indicate that the water injection scenario is effective in preventing the GOR increase by maintaining reservoir pressure, thereby sustaining production over a longer period. This scenario also improves oil recovery by approximately 6% compared to the base case. Finally, optimization was carried out using the DECE optimizer for each scenario to fine-tune the operational parameters. The goal was to maximize oil revenue for each scenario during the optimization process. This study stands out as one of the few that provides a comprehensive analysis of production behavior and development planning for a real fractured reservoir with high producing GOR.

Production systems play a vital role in maximizing consumer satisfaction by efficiently transforming inputs such as labor, raw materials, and capital into products or services aligned with consumer demands. An order-based production takes place in poultry meat and meat products production facilities, which face various difficulties in meeting changing customer demands and managing the supply of raw materials. To optimize production and increase customer loyalty, these facilities use strategic scheduling, considering their daily production capacity and fluctuating customer orders. In this study, estimating which customer and product type the future order quantities will come from for the relevant facilities, increasing customer satisfaction by facilitating order processes and minimizing storage costs are discussed. With this study, the number of orders was estimated, and it was aimed to meet the orders in the most accurate way. In the estimations, the order data of a poultry meat and meat products production facility between 2013 and 2021 were used. Since the order figures will change every year in cases such as the customer working with the facility, growing, or shrinking, better results have been tried to be obtained with the arrangements made on the data set used and three different data sets have been obtained. Estimation processes were performed for these three data sets using LSTM and Prophet algorithms. While the RMSE value was 7.07 in the LSTM model in experimental studies, this value was obtained as 10.96 for Prophet. In the results obtained, it was observed that the arrangements made on the data set positively affected the accuracy of the estimations and the LSTM algorithm produced better results than the Prophet algorithm.

Long-term activity of mining enterprises causes the necessity to substantiate the strategies of management of the mining and technical system functioning in terms of improvement of ore quality control, which is determined by its change in the course of field development due to the priority development of the main reserves and, as a consequence, forced transition to the mining of complex structural rock blocks with a decrease in the recovery percentage, which is typical in case the ore component meets the requirements of the feasibility study in terms of grade at substandard capacity. In this case, it is possible to identify the recovery percentage and the potential for its increase by analyzing the long-term activity of the mining and industrial enterprise, namely, by analyzing the data of mining complex structural rock blocks with the subsequent establishment of the relationship between the primary data on mining and geological conditions and information on the quality of the mineral obtained from the technological equipment. Therefore, the purpose of the research was to substantiate the necessity of improving the management strategy of the mining-technical system functioning, which consists in the fact that on the basis of analyzing the mining data of complex structural rock blocks it is possible to determine the ore mass losses and their quantity and to lay the basis for the development of decisions on its extraction. For this purpose, the collected data on the mining of complex structural rock blocks, accounting the geological and industrial type of extracted ores, were considered in modeling the conditions and studying the parameters of technological processes, the implementation of which provides additional products. It was revealed that the ore mass from substandard thickness layers is delivered to the dumps, and ore mass losses have been estimated at 25-40 % per year. It is proved that determination of ore mass losses based on the analysis of data on mining of complex structural rock blocks, as well as timely solution of this issue can significantly increase the production efficiency of mining and technical system. Taking into account for the results obtained, the options for optimizing the production of the mining and engineering system were proposed.

One of the important tasks of analyzing oil field development is predicting well performance. For this purpose, displacement characteristics are often used, which represent the dependence of some indicators on others. To determine the parameters of these dependencies, regression analysis of historical data is used. Dependences of the choice of watering production wells with water pumped into injection wells, water or the law of the exhausted aquifer.A feature of displacement characteristics is generally considered to be that they can only be used when fluid flows in the formation are established. This is due to the fact that with the classical approach, displacement of characteristics is not observed in the explicit form of well interference. Therefore, the search for displacement characteristics, with the help of which we can talk about the mutual influence of wells, is an important factor. This is the subject of this work.Water cut and water-oil ratio (WOR) are related by a well-known formula. The paper proposes regression models for WOR. They obtained the result taking into account the classical logic of the WOR from accumulated oil production.Water cut is calculated from water saturation. The proposed regression models of water saturation are based on the analysis of equations of theories of two-phase filtration in difference form.11 watering models were studied, two including classical ones and 9 new ones. Dependencies for reservoir and bottomhole pressures were also developed. The proposed models are intended to analyze the operation of wells during the development of an oil reservoir in an elastic-water-pressure mode. The models were tested on a real field and their effectiveness was analyzed. Some new models perform well in a selection of tests. In particular, all the proposed models give better results than the classical model: the logarithm of the water-oil ratio from the accumulation of oil production.

The development of offshore oil fields confronts challenges associated with high water cut and low displacement efficiency. Reservoir injection-production optimization stands out as an effective means to reduce costs and enhance efficiency in offshore oilfield development. The process of optimizing injection and production in offshore oil reservoirs involves designing strategies for a large number of wells and optimization time steps, constituting a large-scale, complex, and costly optimization computation problem. In recent years, with the rapid advancements in big data and artificial intelligence technologies, sophisticated evolutionary computation methods have found widespread application in reservoir injection-production optimization problems. However, the abundance of intelligent optimization algorithms raises the question of how to choose a method suitable for the complex optimization background of offshore oilfield injection-production optimization. This paper provides a detailed overview of the application of an existing differential evolution algorithm (DE), conventional surrogate-assisted evolutionary algorithm (CSAEA), and global–local surrogate-assisted differential evolution (GLSADE) in the context of practical offshore oilfield injection-production optimization problems. A comprehensive comparison of their performance differences is presented. The study concludes that the global–local surrogate-assisted evolutionary algorithm is the most suitable method for addressing the current challenges in offshore oilfield injection-production optimization. [ABSTRACT FROM AUTHOR]

Decisions regarding problem conceptualization, search approach, and how best to parametrize optimization methods for practical application are key to successful implementation of optimization approaches within georesources field development projects. This work provides decision support regarding the application of derivative-free search approaches for concurrent optimization of inflow control valves (ICVs) and well controls. A set of state-of-the-art approaches possessing different search features is implemented over two reference cases, and their performance, resource requirements, and specific method configurations are compared across multiple problem formulations for completion design. In this study, problem formulations to optimize completion design comprise fixed ICVs and piecewise-constant well controls. The design is optimized by several derivative-free methodologies relying on parallel pattern-search (tAPPS), population-based stochastic sampling (tPSO) and trust-region interpolation-based models (tDFTR). These methodologies are tested on a heterogeneous two-dimensional case and on a realistic case based on a section of the Olympus benchmark model. Three problem formulations are applied in both cases, i.e., one formulation optimizes ICV settings only, while two joint configurations also treat producer and injector controls as variables. Various method parametrizations across the range of cases and problem formulations exploit the different search features to improve convergence, achieve final objectives and infer response surface features. The scope of this particular study treats only deterministic problem formulations. Results outline performance trade-offs between parallelizable algorithms (tAPPS, tPSO) with high total runtime search efficiency and the local-search trust-region approach (tDFTR) providing effective objective gains for a low number of cost function evaluations. tAPPS demonstrates robust performance across different problem formulations that can support exploration efforts, e.g., during a pre-drill design phase while multiple independent tDFTR runs can provide local tuning capability around established solutions in a time-constrained post-drill setting. Additional remarks regarding joint completion design optimization, comparison metrics, and relative algorithm performance given the varying problem formulations are also made. [ABSTRACT FROM AUTHOR]

We as a DOOH company have developed a customized LED module which needs less resources thus lowering complexity and raising flexibility in various DOOH projects. This specific LED module saves power, require fewer spare parts and reduces logistic which results in lower overall costs. [ABSTRACT FROM AUTHOR]

In terms of the collaborative optimization of CO2 flooding for Enhanced Oil Recovery (EOR) and CO2 sequestration, previous studies have co-optimized both cumulative oil production and CO2 sequestration by various algorithms. However, these solutions fail to optimize the CO2 injection schemes for high-water cut oil reservoirs. This paper presents an optimization methodology for CO2 flooding and sequestration in high-water cut oil reservoirs. The production optimization was carried out by adjusting the injection and production rate. To solve the proposed objective functions, the simultaneous perturbation stochastic approximation (SPSA) algorithm is applied in this paper, and the CMG-GEM module is utilized to simulate the reservoir production performance. A typical high-water cut reservoir in the Shengli oilfield was used to verify the feasibility of the presented methodology. In this paper, the production performance and net present value (NPV) for continuous gas injection under different water cuts were analyzed. The optimal timing of transforming from water flooding to gas displacement for the high-water cut reservoir was optimized. In addition, the optimal water–gas ratios for Water-Alternating-Gas (WAG) flooding were determined. The sensitivity of NPV to gas injection price and carbon subsidy was analyzed. The results show that when the gas price is 0.178 $/m3 and the carbon subsidy is 0.0169 $/m3, the optimal timing of transforming from water flooding to gas injection should be earlier than the time when the water cut is 0.82. Through the combination of NPV, cumulative oil production rate, and CO2 sequestration volume for WAG flooding, the optimal WAG ratio should be 1:2. The presented method in this paper considers various economic indicators and can optimize CO2 flooding and sequestration in high-water cut oil reservoirs efficiently, which can provide some guidance for the design of CO2 flooding schemes in high-water cut oil reservoirs. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciencia y Tecnología Agropecuaria is the property of Agrosavia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a pioneering method for real-time image processing in electronic component assembly, revolutionizing quality control in manufacturing. By promptly capturing images from pick-and-place machines during the interval between component pick-up and mounting, defects are identified and promptly addressed in line. This proactive approach ensures that defective components are rejected before mounting, effectively preventing issues from ever occurring, thus significantly enhancing efficiency and reliability. Leveraging rapid network protocols such as gRPC and orchestration via Kubernetes, in conjunction with C++ programming and TensorFlow, this approach achieves an impressive average turnaround time of less than 5 milli-seconds. Rigorously tested on 20 operational production machines, it not only ensures adherence to IPC-A-610 and IPC-STD-J-001 standards but also optimizes production efficiency and reliability. [ABSTRACT FROM AUTHOR]

In the field of corrugated board production and packaging optimization, the advent of Artificial Intelligence (AI) has initiated a paradigm shift. This paper presents a brief analysis of AI's role in revolutionizing both the production of corrugated board and the design of corrugated packaging. It explores the integration of AI in the homogenization process of complex corrugated board structures into single-layer, shallow shell-based computational models, aiming to improve and accelerate load-bearing calculations. This work presents also how AI's predictive and analytical capabilities are pivotal in achieving efficiency, sustainability, and costeffectiveness in the corrugated board industry. [ABSTRACT FROM AUTHOR]

Chemical process control relies on a tightly controlled, narrow range of margins for critical variables, ensuring process stability and safeguarding equipment from potential accidents. The availability of historical process data is limited to a specific setpoint of operation. This challenge raises issues for process monitoring in predicting and adjusting to deviations outside of the range of operational parameters. Therefore, this paper proposes simulation-assisted deep transfer learning for predicting and optimizing the final purity and production capacity of the glycerin purification process. The proposed network is trained by the simulation domain to generate a base feature extractor, which is then fine-tuned using few-shot learning techniques on the target learner to extend the working domain of the model beyond historical practice. The result shows that the proposed model improved prediction performance by 24.22% in predicting water content and 79.72% in glycerin prediction over the conventional deep learning model. Additionally, the implementation of the proposed model identified production and product quality improvements for enhancing the glycerin purification process. [ABSTRACT FROM AUTHOR]

A well can be produced and exploited when it has production power, in other words, if the well does not have enough pressure, it will not be able to flow. Artificial production is a method to increase the lifespan of well production. The well studied in this article has a significant annual lower pressure drop; So now with the current pressure of the existing well, it is not possible to send the oil of this well to the separator of the first stage of the exploitation unit. Among the existing solutions to maintain or increase production and increase the flow pressure of the well, is the use of artificial extraction. In this article, an attempt has been made to simulate the effect of using a core pipe, gas pumping and installing an electric submersible pump (ESP) on the production flow rate and flow pressure of the well by using the well data and the static and current pressure test. The current production data with the PIPESIM software was checked, and then the best extraction method for the studied well. Finally, the installation of an ESP was determined and selected as the best method of artificial extraction.

Sericulture is the cultivation of silkworms to produce silk using white mulberry leaves (Morus alba L.); thus, the sector's quality production of mulberry seedlings on a large scale is economically important. The production of seedlings through cuttings is the most effective and fastest reproduction method. Still, it can also be enhanced using synthetic rooting agents, increasing the productive potential of white mulberry crops. The current work aims to evaluate the response of cuttings taken from different parts of the branch of M. alba var. Miura (Apical 1, Apical 2, Medial, Basal 1, and Basal 2) and treated with indolebutyric acid (IBA; 500, 1,000, and 1,500 mg L-1) and commercial rooting agent Forth® (10 % v/v), in addition to a control treatment. After treating the branches, they were placed in polyethylene containers filled with substrate and kept in a greenhouse for 70 days. The application of hormones on cuttings of M. alba favored several morpho-agronomic parameters evaluated for the propagules obtained from different positions on the branch. In conclusion, applying 500 or 1,000 mg L-1 of IBA is recommended for the various positions, with Basal 1 and Basal 2 showing higher values ​​in the evaluated parameters than the other positions. The results can help silk breeders optimize white mulberry crop production.

Abstract In terms of the collaborative optimization of CO2 flooding for Enhanced Oil Recovery (EOR) and CO2 sequestration, previous studies have co-optimized both cumulative oil production and CO2 sequestration by various algorithms. However, these solutions fail to optimize the CO2 injection schemes for high-water cut oil reservoirs. This paper presents an optimization methodology for CO2 flooding and sequestration in high-water cut oil reservoirs. The production optimization was carried out by adjusting the injection and production rate. To solve the proposed objective functions, the simultaneous perturbation stochastic approximation (SPSA) algorithm is applied in this paper, and the CMG-GEM module is utilized to simulate the reservoir production performance. A typical high-water cut reservoir in the Shengli oilfield was used to verify the feasibility of the presented methodology. In this paper, the production performance and net present value (NPV) for continuous gas injection under different water cuts were analyzed. The optimal timing of transforming from water flooding to gas displacement for the high-water cut reservoir was optimized. In addition, the optimal water–gas ratios for Water-Alternating-Gas (WAG) flooding were determined. The sensitivity of NPV to gas injection price and carbon subsidy was analyzed. The results show that when the gas price is 0.178 $/m3 and the carbon subsidy is 0.0169 $/m3, the optimal timing of transforming from water flooding to gas injection should be earlier than the time when the water cut is 0.82. Through the combination of NPV, cumulative oil production rate, and CO2 sequestration volume for WAG flooding, the optimal WAG ratio should be 1:2. The presented method in this paper considers various economic indicators and can optimize CO2 flooding and sequestration in high-water cut oil reservoirs efficiently, which can provide some guidance for the design of CO2 flooding schemes in high-water cut oil reservoirs.

In the field of corrugated board production and packaging optimization, the advent of Artificial Intelligence (AI) has initiated a paradigm shift. This paper presents a brief analysis of AI's role in revolutionizing both the production of corrugated board and the design of corrugated packaging. It explores the integration of AI in the homogenization process of complex corrugated board structures into single-layer, shallow shell-based computational models, aiming to improve and accelerate load-bearing calculations. This work presents also how AI's predictive and analytical capabilities are pivotal in achieving efficiency, sustainability, and cost-effectiveness in the corrugated board industry.

This research contributes to a innovative approach to address the increasing issues of workplace mental health and stress, particularly in high-pressure environments like assembly lines which also affects workers performance and companies productivity. Recognizing the harmful effects of stress on worker productivity, this study introduce stress-monitoring model using advanced machine learning techniques. Proposed model integrates Internet of Things (IoT) technology and machine learning techniques, utilizing a wearable watch to gather open-source physiological data indicative of workers stress in assembly lines. Key physiological markers, such as heart rate, respiration rate, and skin conductance, are analyzed. Based on these physiological indicators, the primary objective is to develop and validate framework that can accurately predict worker stress levels using IoT and machine learning models. The empirical results of the proposed approach demonstrate that the most effective model achieves an impressive accuracy score, with the XGBoost model providing 99% accuracy and Matthew’s correlation coefficient (MCC) of 0.99, surpassing the performance of Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM). The practical implications of these findings suggest a significant potential for implementing such technology in high-stress work settings, offering a proactive tool for stress management and contributing to enhanced worker well-being and productivity.

Copyright of Iraqi Journal of Chemical & Petroleum Engineering is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Different from conventional gas reservoirs, the permeability of coalbeds is affected by stress sensitivity and matrix shrinkage during production. These two conditions lead to lower permeability in the reservoir and affect the production efficiency of the gas well. In addition, coalbed methane wells have single-phase water flow in the initial stage of production. When the reservoir pressure is reduced to its critical desorption pressure, the adsorbed gas in the reservoir desorbs into the pore space and participates in the flow. The flow state in the reservoir changes from single-phase to two phase, and the permeability of the reservoir decreases. The occurrence of these three damage mechanisms is related to the flow rate of fluid in the reservoir. At present, there is a lack of research on the optimization of drainage and production systems considering the damage mechanism of coal reservoirs. This study comprehensively considers how to optimize the production rate of coal reservoirs under the influence of stress sensitivity, matrix shrinkage and two phase flow in the production process to achieve the purpose of production with the least damage to the reservoir. [ABSTRACT FROM AUTHOR]

Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems. However, existing research suggests that the effectiveness of a surrogate model can vary depending on the complexity of the design problem. A surrogate model that has demonstrated success in one scenario may not perform as well in others. In the absence of prior knowledge, finding a promising surrogate model that performs well for an unknown reservoir is challenging. Moreover, the optimization process often relies on a single evolutionary algorithm, which can yield varying results across different cases. To address these limitations, this paper introduces a novel approach called the multi-surrogate framework with an adaptive selection mechanism (MSFASM) to tackle production optimization problems. MSFASM consists of two stages. In the first stage, a reduced-dimensional broad learning system (BLS) is used to adaptively select the evolutionary algorithm with the best performance during the current optimization period. In the second stage, the multi-objective algorithm, non-dominated sorting genetic algorithm II (NSGA-II), is used as an optimizer to find a set of Pareto solutions with good performance on multiple surrogate models. A novel optimal point criterion is utilized in this stage to select the Pareto solutions, thereby obtaining the desired development schemes without increasing the computational load of the numerical simulator. The two stages are combined using sequential transfer learning. From the two most important perspectives of an evolutionary algorithm and a surrogate model, the proposed method improves adaptability to optimization problems of various reservoir types. To verify the effectiveness of the proposed method, four 100-dimensional benchmark functions and two reservoir models are tested, and the results are compared with those obtained by six other surrogate-model-based methods. The results demonstrate that our approach can obtain the maximum net present value (NPV) of the target production optimization problems. [ABSTRACT FROM AUTHOR]

The growing intricacies in engineering, energy, and geology pose substantial challenges for decision makers, demanding efficient solutions for real-world production. The water flow optimizer (WFO) is an advanced metaheuristic algorithm proposed in 2021, but it still faces the challenge of falling into local optima. In order to adapt WFO more effectively to specific domains and address optimization problems more efficiently, this paper introduces an enhanced water flow optimizer (CCWFO) designed to enhance the convergence speed and accuracy of the algorithm by integrating a cross-search strategy. Comparative experiments, conducted on the CEC2017 benchmarks, illustrate the superior global optimization capability of CCWFO compared to other metaheuristic algorithms. The application of CCWFO to the production optimization of a three-channel reservoir model is explored, with a specific focus on a comparative analysis against several classical evolutionary algorithms. The experimental findings reveal that CCWFO achieves a higher net present value (NPV) within the same limited number of evaluations, establishing itself as a compelling alternative for reservoir production optimization. [ABSTRACT FROM AUTHOR]

Production systems play a vital role in maximizing consumer satisfaction by efficiently transforming inputs such as labour, raw materials, and capital into products or services aligned with consumer demands. An order-based production takes place in poultry meat and meat products production facilities, which face various difficulties in meeting changing customer demands and managing the supply of raw materials. To optimize production and increase customer loyalty, these facilities use strategic scheduling, considering their daily production capacity and fluctuating customer orders. In this study, estimating which customer and product type the future order quantities will come from for the relevant facilities, increasing customer satisfaction by facilitating order processes and minimizing storage costs are discussed. With this study, the number of orders was estimated, and it was aimed to meet the orders in the most accurate way. In the estimations, the order data of a poultry meat and meat products production facility between 2013 and 2021 were used. Since the order figures will change every year in cases such as the customer working with the facility, growing, or shrinking, better results have been tried to be obtained with the arrangements made on the data set used and three different data sets have been obtained. Estimation processes were performed for these three data sets using LSTM and Prophet algorithms. While the RMSE value was 7.07 in the LSTM model in experimental studies, this value was obtained as 10.96 for Prophet. In the results obtained, it was observed that the arrangements made on the data set positively affected the accuracy of the estimations and the LSTM algorithm produced better results than the Prophet algorithm. [ABSTRACT FROM AUTHOR]

Purpose. This paper aims to investigate the process of modeling and simulating the mill plant operations with a specific emphasis on the use of AggFlow software. The main purpose is to highlight the importance of modern approaches in to mill plant operation, with a focus on the crucial role of simulation in improving production processes, reducing inefficiencies and optimizing resource use. Methods. The AggFlow software is used to model current operations at a mill plant in Jordan with a specific emphasis on the limestone production in different size fractions. The accuracy of the simulation is verified by carefully comparing it with actual operational data, confirming the AggFlow effectiveness in predictive modeling to enhance mill plant performance. Findings. This study has systematically increased the production rates of mill plant products through thorough analysis while ensuring that the supplying conditions remain consistent. The aim was to increase production efficiency while guaranteeing the marketability of the finished products. The findings provided useful insights into effective operational modifications and strategies for enhancing production rates while maintaining product quality. Originality. This research provides novel insights by integrating actual mill plant operations with sophisticated simulation utilizing AggFlow software. The study confirms the reliability of AggFlow as a tool for predicting models and offers new insights into enhancing production efficiency in mill plant environments. Practical implications. The research results are directly applicable to mill plant operators, providing a realistic method for improving operational efficiency through the use of AggFlow simulation. The research provides practical methods that can be implemented to optimize production rates and maintain consistent product quality in mill plant operations. [ABSTRACT FROM AUTHOR]

The modern world has entered an acute phase of irreversible changes and implementation of global economic transformation. In today's situation, a sensible, coherent, flexible and adaptable national economic strategic doctrine created by the state, delineating the proper role and function of the country's regional and agrarian policy, is becoming urgent. The Strategy of Agriculture and Rural Development of Georgia for 2021-2027 considers the creation of competitive products the main objective, however, the single action plan and the service programs available to the Ministry today do not leave the feeling that we have understood all the problems and challenges that arise at different stages, step or phase. Based on the foregoing, it is appropriate to raise the issue of policy options to promote production of agricultural products in the regions. Georgia's agriculture and regional development are moving absolutely haphazardly. It is this kind of inertia that needs to be stopped and urgently reformed and transformed, where the determination of the “rules of the game” of the institutional economy and the activity of regional structures in terms of statistical-analytical and situational-contextual studies become a prerequisite for targeted-program actions and the development of the field. It I safe to say that creating a regional agriculture and rural development strategy is an urgent and at the same time difficult task in today's situation. First of all, the problem is specified in the area of –administrative-territorial system and imperfections of legislation. As for the socio-economic aspects and the range of actions and thematic diversity here encourage us to think creatively and determine the advantages of causal programs. Contemporary world challenges in recent years show even more attention to issues of regionalization and the introduction of decentralization-deconcentration principles in the processes generally. The programs developed in the regions, which should be formed according to the “bottom-up” principle, fall under the institutional order network, because they become a continuation of the centripetal macroeconomic strategy of the government and create the needs of the centrist policy with the help of the principles of the regional economy. It is of the utmost importance that flexible and effective institutional changes appear in the regions, thus we will be able to analyze the resource potential and prospeects of the economy, motivate active behavior, which will replace evolutionary inertia and fragmentation with a complex, structurally-organizationally formed, technical-economically sound policy. The five directions of agricultural production optimization presented in the paper, starting from the land optimization formula and ending with the importance of introducing innovative technologies, are a single complex action module, where each has its own unique function-duty, and it is not unacceptable to disable or ignore them from a business and economical point of view.

Abstract Colorectal cancer (CRC) is the third cause of death by cancers worldwide and is one of the most common cancer types reported in both Egypt and the United States. The use of probiotics as a dietary therapy is increasing either as a prevention or as a treatment for many diseases, particularly, in the case of CRC. The increasing acceptance of lactic acid bacterial (LAB) oligosaccharides as bioactive agents has led to an increase in the demand for the large-scale production of LAB-oligosaccharides using fermentation technology. Therefore, in the current study, we are using the Plackett- Burman design (PBD) approach, where sixteen experimental trials were applied to optimize the production of the target oligosaccharide LA-EPS-20079 from Lactobacillus acidophilus. Glucose, yeast extract and sodium acetate trihydrate were the top three significant variables influencing LA-EPS production. The maximum concentration of LA-EPS-20079 achieved by L. acidophilus was 526.79 μg/ml. Furthermore, Box–Behnken design (BBD) as response surface methodology (RSM) was used to complete the optimization procedure. The optimal levels of the chosen variables which were 30.0 g/l, glucose; 5 g/l, yeast extract and 10.0 g/l sodium acetate trihydrate with the predicted LA-EPS-20079 concentration of 794.82 μg/ml. Model validity reached 99.93% when the results were verified. Both optimized trials showed great cytotoxic effects against colon cancer line (CaCo-2) with inhibition percentages ranging from 64.6 to 81.9%. Moreover, downregulation in the expression level of BCL 2 and Survivin genes was found with a fold change of 3.377 and 21.38, respectively. Finally, we concluded that the optimized LA-EPS-20079 has maintained its anticancer effect against the CaCo-2 cell line that was previously reported by our research group.

An optimization study was conducted to determine the optimal operating pressure for the oil and gas separation vessels in the West Qurna 1 oil field. The ASPEN HYSYS software was employed as an effective tool to analyze the optimal pressure for the second and third-stage separators while maintaining a constant operating pressure for the first stage. The analysis involved 10 cases for each separation stage, revealing that the operating pressure of 3.0 Kg/cm2 and 0.7 Kg/cm2 for the second and third stages, respectively, yielded the optimum oil recovery to the flow tank. These pressure set points were selected based on serval factors including API gravity, oil formation volume factor, and gas-oil ratio from the flow tank. To improve the optimization process for separator sizes, a Python code was developed, combining the Newton Raphson Method (NRM), and Lang Cost Method (LCM), with Retention time calculations. In this process, total purchase cost was the objective function. Two design scenarios were examined, corresponding to throughput of 105,000 KBPD and 52,500 KBPD respectively. In the first scenario, the NRM, LCM, and Retention time methods within the Python code were employed, resulting in a three-stage separation train with costs of $1,534,630 for the first stage, $1,438,239 for the second stage and $1,025,978 for the third stage. The Total purchase cost for the separation train was $3,988,847. In the second scenario, utilizing two separators for each stage to process the same throughput resulted in lower costs, totaling $823,851.5 per stage and a total purchase cost of $2,471,553. These costs were calculated using the Lang Cost method, which included the material cost and utilized a Lang factor of 3.1 to determine the total purchase cost after adding shipping, installation, commissioning, and start-up expenses. The first scenario resulted in larger separators and higher costs, while the second scenario showed lower costs, although it required two vessels per stage to process the same throughput. It was observed that the separator efficiencies were influenced by retention time, with increased retention time leading to improved separator efficiency.

Since complex industrial chemical systems contain huge process data, these process data will present the operation characteristics and laws, and the use of appropriate methods to analyze the data is one of the feasible directions for fault diagnosis. In this paper, we analyze data from industrial chemical production processes using the support vector machine algorithm as our decision-making approach. Considering the large amount of data generated in industrial chemical systems and its nonlinear characteristics, this study applies Gaussian and non-Gaussian space to obtain the high-dimensional characteristics of the data before putting it into an SVM model. The rotary drying kiln production simulation experimental process is optimized by applying the fault diagnosis method, with the thermal efficiency shown by several tests being close to each other. The method proposed in this paper has obvious advantages in terms of search efficiency for a feasible global optimal solution.

Tight oil reservoirs suffer from a high decline in flow rates and instability of production rates, even after implementing the hydraulic fracturing technique to increase the production rate, but the problem still stands and needs to be overcome. This issue is found in the southeast of Iraq in the Halfaya oil field, especially in the Sadi formation. The goal of this study is to identify an optimum production rate for horizontal and vertical hydraulic fracturing wells using simulator software for prediction, in order to avoid highly depleted fracture storage capacity, which results from high production rates and depletion in flowing pressure. as well as to discuss the production behavior of the wells over their life cycles in the Sadi reservoir. In addition, we utilize a novel approach with production rates for horizontal hydraulic fracturing wells to reach a stable production rate. The results show that the production behavior of hydraulic fracturing wells is clear: producing at a high flow rate depletes the fluid potential of a fracture without providing a reservoir with an opportunity to compensate fluid into fracturing potential. In horizontal hydraulic fracturing wells, conducting an allowable flow rate that satisfied stability. We conclude a suitable flow rate has been identified to avoid high depletion rates and the plateau rate was different than that detected by the operator company, and we advise on future hydraulic fracture well development. [ABSTRACT FROM AUTHOR]

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The rudimentary findings associated with the optimum operation of a gas reservoir highlight the interdependence of the network of fractures, rock matrix, and reservoir permeability. However, if due to any drilling issue, the well is drilled in a section where it does not encounter any fracture, or if the fracture network is disconnected from the matrix, productivity is greatly reduced. Stimulation methods (matrix acidizing and acid fracturing) are deployed to restore/enhance the reservoir permeability to improve the productivity of a reservoir. Stimulation methods play a crucial role in addressing the challenge of restoring and enhancing a reservoir's capability to improve production. Acidizing and fracturing are the two stimulation methods commonly used. The selection of these methods mainly depends on the type of reservoir and its characteristics. In this paper, the effects of matrix acidizing and acid fracturing on a carbonate reservoir are presented. STIMPRO® designed for matrix acidizing/analysis and FRACPRO® designed for fracture geometry analysis enabled the convergence of the actual well data. An acid chemical recipe is designed to execute the matrix fracturing treatment. The stimulation diversion technique, maximized pressure differential and injection rates (MAPDIR), is used to inject the cited acid chemical recipe at designed injection rates in the reservoir. Well test analysis using ECRIN® and production optimization through PIPESIM® are carried out to maximize the efficiency of the matrix acidizing and acid fracturing. The well production increased fourfold to 1 MMSCFD post-matrix acidizing job. The overall well production of 14.6 MMSCFD was observed post-acid fracturing employing the designed acid recipe with MAPDIR diversion. The economic viability of both the treatments was evaluated using the decline curve analysis (DCA) technique which showed a considerable increase in low and best estimates (1P, 2P) of reservoirs post-acid fracturing jobs. [ABSTRACT FROM AUTHOR]

In binary multi-objective well placement optimization, multiple conflicting objective functions must be optimized simultaneously in reservoir simulation models containing discrete decision variables. Although multi-objective algorithms have been developed or adapted to tackle this scenario, such as the derivative-free evolutionary algorithms, these methods are known to generate a high number of duplicated strategies in discrete problems. Duplicated strategies negatively impact the optimization process since they: (i) degrade the efficiency of recombination operators in evolutionary algorithms; (ii) slow the convergence speed as they require more iterations to find a well-distributed set of strategies; and (iii) perform unnecessary re-evaluations of previously seen strategies through reservoir simulation. To perform multi-objective well placement optimization while avoiding duplicated strategies, this paper investigates the application of a newly proposed algorithm named MOEA/D-NFTS, with a modified diversity preservation mechanism that incorporates prior knowledge of the problem, on a multi-objective well placement optimization problem. The proposed methodology is evaluated on the UNISIM-II-D benchmark case, a synthetic carbonate black-oil simulation model in a well placement optimization problem using a binary strategy representation, indicating the presence or absence of a given candidate well position in the final strategy. The objective functions are the maximization of the Net Present Value, the maximization of the Cumulative Oil Production, and the minimization of Cumulative Water Production. The modified MOEA/D-NFTS performance is compared with a baseline algorithm without diversity preservation, and the evidence shows that the MOEA/D-NFTS produces statistically significant superior results, and is suitable for binary multi-objective well placement optimization. [ABSTRACT FROM AUTHOR]