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The article presents the results of work on the application of the WASPAS method in the selection of optimal parameters of the water-abrasive cutting process, which significantly affect the course of the research process, while allowing the user to omit those factors that have a negligible impact on the results. WASPAS is a multi-criteria decision-making technique. This is a weighted aggregate product evaluation sum method for parametric optimization. A method can indicate one or more optimization responses. It combines the Weighted Sum Model (WSM) and the Weighted Product Model (WPM). The article shows that WASPAS effectively determines a set of optimal combinations of parameters for many responses. The material used in the study was Inconel-625- a nickel-chromium alloy. It was found that the rate of material removal varies directly with feed rate and abrasive flow rate. Increasing the feed rate and abrasive flow rate increased the rate of workpiece material removal during waterjet cutting. The abrasive flow rate and the feed speed had the greatest influence on the roughness. The advantage of proceeding with the WASPAS scheme is the selection of parameters that can be very carefully optimized by applying an additional factor.Ñú.. The method was considered more meticulous compared to others. [ABSTRACT FROM AUTHOR]

In recent years, manufacturing companies have been increasingly confronted with rising demands relating to their production, uncertain conditions in their environment and volatile markets. To fulfil the diversified customers' demands and rapidly changing markets, manufacturing companies need to achieve and maintain high productivity and quality. This requires fast responses, sufficient flexibility and short lead times. Therefore, flexible, resilient and customer-oriented production processes are becoming more and more important. Current optimization measures of industrial environments focus primarily on increasing efficiency while reducing costs. The reliance on other factors, such as a resilient as well as economically, ecologically and socially sustainable manufacturing system, has not been sufficiently taken into account yet. Bridging the gap between research and practice, this paper proposes a holistic approach that assesses resilience, sustainability and efficiency measures of manufacturing companies. A multi-criteria assessment model is developed to stabilize qualitative and quantitative measures within these dimensions. Furthermore, the model is tested in an Austrian small and medium-sized (SME) enterprise in the wood processing industry. The targeted groups are SMEs and multinational companies. [ABSTRACT FROM AUTHOR]

• The established deep learning model is capable of considering the impacts of 3D heterogeneity of oil shale on production and energy consumption. • The optimization procedure simultaneously optimized well constraints and well locations. • The evolution patterns of optimal parameter values across varying levels of energy constraints are identified. The in-situ conversion process (ICP) involves complicated thermal-reactive-compositional flow processes where the production parameters are closely interdependent. Therefore, manual optimization of the production parameters of ICP through numerical simulation is arduous and time-consuming. This paper introduces a computational framework that integrates deep learning techniques and particle swarm optimization (PSO) to automatically optimize the values of production parameters of ICP, and thus locating the highest energy efficiency and the optimum energy usage. This approach utilizes a 3D CNN model to predict key metrics. The prediction process considers the detailed 3D heterogeneity of oil shale, resulting in remarkably high prediction accuracy, as evidenced by determination coefficients (R2) above 0.99. Subsequently, the trained CNN model is integrated to the PSO algorithm to automatically fine-tune the production parameters to optimize the energy efficiency of ICP. The optimization results yield three significant findings. First, as the energy consumption limit increases, the optimal number of heaters, well spacing, and heating temperature exhibit an upward trend, but stabilized beyond the threshold of 7 × 105 kWh. Secondly, the optimal input energy (7 × 105 kWh) is found for the given ICP model. Lastly, the analysis reveals that variations in initial reservoir pressure or bottomhole pressure have limited impact on cumulative oil production and energy usage. [ABSTRACT FROM AUTHOR]

Primal–dual feedback-optimizing control is a simple yet powerful approach to optimally handle active constraint changes at steady state. It is composed of two layers: Constraint control in the upper master layer and unconstrained optimization or gradient control in the layer below. The master constraint controllers operate on a slow time scale by updating the dual variables (Lagrange multipliers). This can result in too slow control of the constraints, for example, for hard constraints that cannot be violated dynamically. To address this issue, we propose introducing a third fast override constraint control layer. Additionally, to optimally coordinate the constraint handling between the master and override layers, we need to introduce auxiliary constraints for the master controllers. A gas-lift oil production optimization case study demonstrates the power of the proposed scheme. • Primal–dual feedback-optimizing control is a simple yet powerful (distributed-) optimization approach to optimally handle active constraint changes at steady state. However, it controls constraint on a slow time scale. • A proposed scheme, primal–dual feedback-optimizing control with override constraint control and auxiliary constraints, can control constraint on a fast time scale, thereby reducing violations of dynamic constraints. • A gas-lift oil production optimization case study demonstrates the power of the proposed scheme. [ABSTRACT FROM AUTHOR]

The use of lignocellulosic biomass to produce value-added products, such as fuel ethanol, is a current priority for global biobased economy. The quest to explore novel microbes, such as yeasts, to attain sustainable products for an ever-growing population has become highly in demand. This study might be the first to explore the rumen yeast Pichia kudria vzevii SVMS2019 for cellulase production to efficiently utilize wheat straw as biomass, which can further be converted to bioethanol. The optimization process was carried out on pre-treated wheat straw by employing one factor at a time (OFAT) and response surface methodology (RSM). Wheat straw pre-treated using NaOH alkali pretreatment was characterized with FTIR, XRD, and SEM studies. The findings revealed that the P. kudriavzevii SVMS2019 obtained maximum cellulase activity of 44.064 IU/ml at 37.5 °C, pH 3.5, 120 h, and 1.5% of NaOH pre-treated wheat straw (substrate concentration) through RSM. It exhibited 1.25-fold increased production compared to OFAT. The stability of crude cellulase was observed at 45 °C and pH 6. Further, the hydrolyzed reducing sugars produced under optimal conditions opted for fermentation, with a maximum bioethanol content of 12.13 g/l obtained at 37.5 °C in a 24 h interval. [Display omitted] [ABSTRACT FROM AUTHOR]

Recombinant proteins used in biomedical research, diagnostics and different therapies are mostly produced in Chinese hamster ovary cells in the pharmaceutical industry. These biotherapeutics, monoclonal antibodies in particular, have shown remarkable market growth in the past few decades. The increasing demand for high amounts of biologics requires continuous optimization and improvement of production technologies. Research aims at discovering better means and methods for reaching higher volumetric capacity, while maintaining stable product quality. An increasing number of complex novel protein therapeutics, such as viral antigens, vaccines, bi- and tri-specific monoclonal antibodies, are currently entering industrial production pipelines. These biomolecules are, in many cases, difficult to express and require tailored product-specific solutions to improve their transient or stable production. All these requirements boost the development of more efficient expression optimization systems and high-throughput screening platforms to facilitate the design of product-specific cell line engineering and production strategies. In this minireview, we provide an overview on recent advances in CHO cell line development, targeted genome manipulation techniques, selection systems and screening methods currently used in recombinant protein production. [ABSTRACT FROM AUTHOR]

The optimization of production systems is a core-business for manufacturing companies. Modern approaches focus on strategic optimization via the concept of the Digital Twin. In this paper we present a model for the operational optimization of manufacturing systems via the core technology of the Digital Shadow. The model combines three main-aspects: automation, universality and the utilization of real-time data. Automation is achieved by a self-developed algorithm, universality by the application of a genetic algorithm and the integration of real-time-data through the utilization of the Digital Shadow. We validate the model by means of experimental-design. [ABSTRACT FROM AUTHOR]

The digitalization in manufacturing offers high potential for optimization in terms of quality and efficiency. In particular, machine learning techniques can be used to analyze data generated along the production chain for complex patterns. As the final product quality highly depends on interactions within the production chain, a process control system needs to consider all information of the supplied semi-finished products to achieve a continuously high quality. Using machine learning, this work presents a two-stage batch control system to optimize a batch process with high interdependencies between the delivered material, used equipment and process parameters concerning the final product quality. [ABSTRACT FROM AUTHOR]

Sheet metal forming that consists of discrete flow processes, is employed in a wide range of applications in industry. Aiming at supporting energy efficient manufacturing in sheet metal forming, an integrated framework is proposed. The framework comprises two layers, namely, operation management and data acquisition. Operation management is performed by three procedures, i.e., energy consumption modeling, energy performance evaluation, and production optimization. Through the operation management, suggestions for saving energy and improving production efficiency are proposed and implemented. Data acquisition is employed to provide useful information. Demonstration analysis in a stamping shop floor validates the feasibility of the proposed framework. [ABSTRACT FROM AUTHOR]

Curium targets are irradiated in high-flux reactors to produce californium-252 (Cf-252). Thermalized neutron spectrum is favorable for Cf-252 production, so traditional methods deploy moderators around the curium targets to optimize the neutron spectrum. However, using moderators alone cannot achieve fine spectrum optimization, and does not necessarily result in better Cf-252 production. We conducted refined neutron spectrum analysis and optimization research to improve production efficiency further. First, based on analysis of the transmutation chain during Cf-252 production, intermediate nuclides with a significant impact on production efficiency, known as "key nuclides," are identified. Then, the "fission-absorption ratio" and "direct loss rate" of these intermediate nuclides are quantitatively analyzed to determine the energy regions that have a greater negative impact on the production process. Finally, filter materials are selected to reduce the neutron flux in these energy regions, optimizing the neutron spectrum around the curium targets and increasing yield and conversion rate during Cf-252 production. The neutron spectrum optimization method for Cf-252 production based on key nuclides analysis helps improve the efficiency and economy of Cf-252 production. • Innovation: three new filter nuclides are proposed for Cf-252 production. • Universality: The analytical method helps the production process of other isotopes. • Feasibility: the conclusion has been verified in the HFIR reactor model. • Significance: the study improves the production efficiency of Cf-252. [ABSTRACT FROM AUTHOR]

• A novel membrane algorithm is proposed for multi-objective optimization problems. • Best objects are communicated and dominated ones are eliminated in competition. • The algorithm produces well-distributed and converged solutions. • The results reduce 2698 tons of feed oil and 8282 tons of carbon dioxide emission. As a relatively high energy-consuming in China, the chemical process is important and has potential to be optimized for energy saving, economic benefits and environmental protection. Multiple goals can be coordinated using multi-objective optimization algorithms. In order to improve the exploration and exploitation abilities in optimizing multiple objectives, a constrained competing evolutionary membrane algorithm is proposed. The proposed algorithm takes advantages of the distributed and parallel computing mode of the membrane computing. Populations are evolved independently in each membrane and share information between membranes based on competing communication rules. Meanwhile, the skin membrane archives global elitist solutions and serves as guidance for inner evolution processes. Finally, the optimization experiments on the ethylene cracking process, as an important production of complex chemical processes, prove that the proposed algorithm can provide enough selection for decision makers with well-distributed and converged candidate solutions. Furthermore, the solutions lead the ethylene cracking process to reach the coordinated optimum ethylene or propylene production, oil consumption reduction and carbon dioxide emission reduction. In average, the optimization solutions bring about reduction of 2697.58 tons of feed oil and 8281.57 tons of carbon dioxide emission. [ABSTRACT FROM AUTHOR]

This paper presents a field-development case study of a tight gas sandstone reservoir in China. The gas production well JK1 in the Linxing-Shenfu Gasfield in Eastern Ordos Basin is concurrent producing from three separate layers, and experienced interlayer interference stage in which gas production dropped to 0 rapidly. The purpose of this study is to investigate the influence of interference between different layers on concurrent production, to explore a reasonable adjustment plan, and finally to enhance the productivity of well JK1. The production history of this well was analyzed and a three-dimensional geological model for it was constructed. The interlayer interference of pay zones and zones combination optimization were simulated for field application. The simulation results show that the interlayer interference is obvious when the three layers have commingled production, the fluids even flow backward to the Upper layer. When the Middle layer is produced with other layers, it is the main water production layer with less gas. The Lower layer has always been the main gas production layer with less water. The optimization results indicate that only the Lower formation produces separately, the gas production is the highest. The optimization of field operational practices was based on the introduction of numerical simulation results enabled a gas production increase from vanishingly low to almost 4500 m3/d in the following year. This study can provide an insight on interlayer interference and a good guidance for a production strategy on full field development. • An embedded grid method is applied to perform elaborate simulations of hydraulic fractures. • An intuitive approach is presented to identify the interference between layers. • The interference between production layers is investigated during concurrent production. • The proposed method is applied in a field case to optimize the production strategy of well JK1. [ABSTRACT FROM AUTHOR]

Highlights • GPS, PSO, and CMA-ES are combined with a multiscale framework. • Settings for GPS, PSO, and CMA-ES, within the multiscale framework are considered. • CMA-ES performs best among the three algorithms without the multiscale framework. • GPS improving the most when hybridized with the multiscale framework. • The effect of two key parameters of the multiscale framework are investigated. Abstract Smart well technologies, which allow remote control of well and production processes, make the problem of determining optimal control strategies a timely and valuable pursuit. The large number of well rates for each control step make the optimization problem difficult and present a high risk of achieving a suboptimal solution. Moreover, the optimal number of adjustments is not known a priori. Adjusting well controls too frequently will increase unnecessary well management and operation cost, and an excessively low number of control adjustments may not be enough to obtain a good yield. In this paper, we explore the capability of three derivative-free algorithms and a multiscale regularization framework for well control optimization over the life of an oil reservoir. The derivative-free algorithms chosen include generalized pattern search (GPS), particle swarm optimization (PSO) and covariance matrix adaptation evolution strategy (CMA-ES). These algorithms, which cover a variety of search strategies (global/local search, stochastic/deterministic search), are chosen due to their robustness and easy parallelization. Although these algorithms have been used extensively in the reservoir development optimization literature, for the first time we thoroughly explore how these algorithms perform when hybridized within a multiscale regularization framework. Starting with a reasonably small number of control steps, the control intervals are subsequently refined during the optimization. Results for the experiments studied indicate that CMA-ES performs best among the three algorithms in solving both small and large scale problems. When hybridized with a multiscale regularization approach, the ability to find the optimal solution is further enhanced, with the performance of GPS improving the most. Topics affecting the performance of the multiscale approach are discussed in this paper, including the effect of control frequency on the well control problem. The parameter settings for GPS, PSO, and CMA-ES, within the multiscale approach, are considered. [ABSTRACT FROM AUTHOR]

Ultra-flexible factories must meet the requirements of mass personalization. The basis is provided by means of modular, mobile machines which can be constantly reconfigured and positioned depending on the current production order. The numerous possible ways the order can take through the production lead to a high complexity, especially for individualized products. Currently, there is a lack of methods to manage this complex order flow and to control the production according to multi-criteria specifications. By combining metrological methods and a waste-oriented manufacturing logic, it is intended to enable production control for flexible value-added systems within specific time and economic tolerances. [ABSTRACT FROM AUTHOR]

Abstract Waterflooding optimization in closed-loop management of the oil reservoirs is always considered as a challenging issue due to the complicated and unpredicted dynamics of the process. The main goal in waterflooding is to adjust the manipulated variables such that the total oil production or a defined objective function, which has a strong correlation with the gained financial profit, is maximized. Fortunately, due to the recent progresses in the computational tools and also expansion of the calculating facilities, utilization of non-conventional optimization methods is feasible to achieve the desired goals. In this paper, waterflooding optimization problem has been defined and formulated in the framework of Reinforcement Learning (RL) methodology, which is known as a derivative-free and also model-free optimization approach. This technique prevents from the challenges corresponding with the complex gradient calculations for handling the objective functions. So, availability of explicit dynamic models of the reservoir for gradient computations is not mandatory to apply the proposed method. The developed algorithm provides the facility to achieve the desired operational targets, by appropriately defining the learning problem and the necessary variables. The fundamental learning elements such as actions, states, and rewards have been delineated both in discrete and continuous domain. The proposed methodology has been implemented and assessed on the Egg-model which is a popular and well-known reservoir case study. Different configurations for active injection and production wells have been taken into account to simulate Single-Input-Multi-Output (SIMO) as well as Multi-Input-Multi-Output (MIMO) optimization scenarios. The results demonstrate that the "agent" is able to gradually, but successfully learn the most appropriate sequence of actions tailored for each practical scenario. Consequently, the manipulated variables (actions) are set optimally to satisfy the defined production objectives which are generally dictated by the management level or even contractual obligations. Moreover, it has been shown that by properly adjustment of the rewarding policies in the learning process, diverse forms of multi-objective optimization problems can be formulated, analyzed and solved. Highlights • A Reinforcement Learning based algorithm is introduced for waterflooding management. • Availability of explicit dynamic models is not mandatory to utilize the proposed technique. • Needless to get involved in gradient computations, to optimize the objective functions. • Various production strategies can be pursued by properly designating the rewarding policy. • A practical R L -based solution for fair profit-sharing among all parties is introduced. [ABSTRACT FROM AUTHOR]

Abstract Reservoir production optimization involves the determination of well control settings which maximize a pre-defined reservoir production preference such as ultimate oil recovery or net present value (NPV). Honoring the nonlinear constraints, such as producer's water cut and bottom hole pressure (BHP) constraints for rate specified wells, is the most challenging part of the production optimization. This work entails a comparative study of the augmented Lagrangian (AL) and sequential quadratic programming (SQP) algorithms for constrained reservoir optimization. Both methods have been widely used to solve the nonlinear constrained optimization problems in the numerical optimization community. The optimization of PUNQ reservoir model is considered in order to compare the performance and robustness of the AL and the SQP, where the gradients of the objective function and nonlinear constraints are estimated using the adjoint method. The computational results showed that with properly tuned parameters and good initial points, the AL could yield a slightly higher ultimate NPV in some cases. However, the SQP generally has better performance regarding efficiency, robustness and constraints handling. For example, under the SQP generated optimal well controls, the watercut of each producer would strictly obey the constraint, while the optimal well controls generated by AL would lead some minor valuations. Moreover, an SQP-AL algorithm is also proposed to solve the constrained production optimization problem by applying the AL search direction on the convergence of SQP. The results showed that the SQP-AL algorithm is able to achieve a higher NPV with slightly more iterations compared with the SQP method. Highlights • A comparative study of the AL and SQP algorithms for constrained reservoir optimization. • Compare the performance of SQP and AL regarding efficiency, robustness and constraints handling. • Propose the SQP-AL algorithm by applying the AL search direction on the convergence of SQP. • Implement the adjoint gradient for 3D 3Phase general reservoir simulator. [ABSTRACT FROM AUTHOR]

Real-time optimization (RTO) is an established technology, where the process economics are optimized using rigourous steady-state models. However, a fundamental limiting factor of current static RTO implementation is the steady-state wait time. We propose a “hybrid” approach where the model adaptation is done using dynamic models and transient measurements and the optimization is performed using static models. Using an oil production network optimization as case study, we show that the Hybrid RTO can provide similar performance to dynamic optimization in terms of convergence rate to the optimal point, at computation times similar to static RTO. The paper also provides some discussions on static versus dynamic optimization problem formulations. [ABSTRACT FROM AUTHOR]

This paper considers the upstream oil and gas domain, or more precisely the daily production optimization problem in which production engineers aim to utilize the production systems as efficiently as possible by for instance maximizing the revenue stream. This is done by adjusting control inputs like choke valves, artificial lift parameters and routing of well streams. It is well known that the daily production optimization problem is well suited for mathematical optimization. The contribution of this paper is a discussion on appropriate formulations, in particular the use of static models vs. dynamic models. We argue that many important problems can indeed be solved by repetitive use of static models while some problems, in particular related to shale gas systems, require dynamic models to capture key process characteristics. The reason for this is how reservoir dynamics interacts with the dynamics of the production system. [ABSTRACT FROM AUTHOR]

The oil and gas (O&G) industry spends billions of dollars on data (e.g., production data, seismic data and tracer data) gathering and analysis for the purpose of reducing uncertainty and improving their understanding of the most salient features of the subsurface. Yet, the O&G industry spends minimal effort and investment to assess whether the benefit of this data gathering and analysis exceeds the cost. One form of data gathering and analysis is history matching (HM), which has been an essential reservoir management tool for decades. This paper addresses the value of HM by applying the value-of-information (VOI) framework originally developed in the decision sciences. There are several challenges involved in assessing VOI in HM contexts. Although reservoir management (HM and production optimization) and decision analysis (DA) use many of the same methods, the two domains involve different terminology used in fluid flow modeling and application of state-of-the-art HM and optimization methods. Furthermore, most applications of VOI analysis have focused on static, as opposed to time-dependent, analysis. Finally, some recent publications in the O&G industry that have illustrated and discussed VOI from HM have not been consistent with the original definition of VOI. In this paper, we illustrate and discuss the use of a consistent, DA-based, VOI analysis framework to assess the VOI in HM contexts. In order to make the VOI framework understandable and accessible to both the reservoir management and DA communities, we provide a “bridge” between the nomenclature and terminology used in VOI calculations and that used in state-of-the-art HM and optimization methods. The paper includes four VOI analysis examples. The first illustrates the implementation of the general VOI framework for a simple HM problem. The second illustrates and discusses the difference between the calculations presented by other authors and the standard VOI definition used in the DA community. The third illustrates the implementation of VOI calculations in more realistic settings, including a sensitivity analysis of measurement noise. The fourth illustrates the application of VOI assessment in a case where a reservoir simulation model is involved. [ABSTRACT FROM AUTHOR]

Typical challenges that managers of remanufacturing face are composed of the lack of timely, accurate, and consistent information of remanufacturing resources. Therefore, it is difficult to implement real-time production scheduling for the shop floor. To address this problem, the authors applied the concept of the ‘Internet of Things’ to the remanufacturing of automobile engines to form an Internet of Manufacturing Things environment. Under the Internet of Manufacturing Things, an identification technology for disassembled engine parts was designed, and the real-time status of the remanufacturable resources can be monitored. Based on the captured remanufacturing information, a real-time production scheduling method was developed, and a mathematical model was developed to achieve cost reduction, dynamic management of remanufacturable resources, and energy consumption decrease. To obtain an optimal solution, a Pareto-based optimization method was used. Finally, a case study was performed to analyze the effectivity of the proposed method. The results showed that the remanufacturing cost and energy consumption were reduced by 34% and 34% respectively, and the worker load rate was more balanced. These improvements can contribute to more sustainable development and greener production within the remanufacturing industry, especially for remanufacturing of automobile engines. [ABSTRACT FROM AUTHOR]

CO 2 water-alternating-gas (WAG) injection is an enhanced oil recovery method designed to improve sweep efficiency during CO 2 injection with the injected water to control the mobility of CO 2 and to stabilize the gas front. Optimization of CO 2 -WAG injection is widely regarded as a viable technique for controlling the CO 2 and oil miscible process. Poor recovery from CO 2 -WAG injection can be caused by inappropriately designed WAG parameters. In previous study (Chen and Reynolds, 2016), we proposed an algorithm to optimize the well controls which maximize the life-cycle net-present-value (NPV). However, the effect of injection half-cycle lengths for each injector on oil recovery or NPV has not been well investigated. In this paper, an optimization framework based on augmented Lagrangian method and the newly developed stochastic-simplex-approximate-gradient (StoSAG) algorithm is proposed to explore the possibility of simultaneous optimization of the WAG half-cycle lengths together with the well controls. The proposed framework is demonstrated with three reservoir examples. [ABSTRACT FROM AUTHOR]

This paper presents a method for efficient optimization of a plunger lift process in shale gas wells. Plunger lift is a cyclic process consisting binary decision as well as continuous and discrete state variables. The time-series data comprising of surface measurements are converted into cycle-wise process-relevant performance outputs, while the binary manipulated variable is transformed into continuous threshold values. These transformed variables are used to develop a reduced order cycle-to-cycle model and corresponding receding horizon optimization problem that maximizes daily production while meeting operational constraints. The efficacy of the proposed algorithm is demonstrated on a simulated plunger lift process. [ABSTRACT FROM AUTHOR]

The design of an optimal gas field development and production management is a complicating task because of influencing various factors on decision-making process. Typical factors include number and type of wells, well locations, and production constraints, economic factors of capital expenditure, operating costs, gas sale price, and different engineering and geological parameters. The situation is further complicated due to uncertainty associated with the nonlinear problem of field development optimization. In this study, first, design of experiment (DOE) techniques including uniform design (UD) and Box-Behnken design (BBD) are used for performance prediction of water drive gas reservoirs. Next, a new metaheuristic bat inspired algorithm (BA) will be applied in optimization process of objective function. Undiscounted net present value (UNPV) is used as an objective function for the determination of optimal number of wells (N), individual well locations (I and J), production rate (Q g ), perforation thickness (H p ) and the limit of tubing head pressure (THP) for different average reservoir permeability (K G ) and permeability anisotropy (K v /K h ). In this regard, part of gas reservoir with an active bottom aquifer based on actual data is simulated in order to predict gas recovery factor and cumulative water production data that are necessary for performing production optimization. The results show that reducing gas production rate increases the UNPV. Reducing production rate increases ultimate recovery factor and decreases cumulative water production. However, considering time value of money, it is favorable to produce with high rates and consequently deplete the reservoir over short time scales in order to compensate the reduction of profit. [ABSTRACT FROM AUTHOR]

There is an intensive investigation reported in the literature regarding the development of robust methods to improve the economical performance during the production management of petroleum fields. One paradigm that emerged in the last decade and has been calling the attention of various research groups is known as closed-loop reservoir management. The closed-loop entails the application of history matching and production optimization in a near-continuous feedback process. This work presents a closed-loop workflow constructed with ensemble-based methods. The proposed workflow consists of three components: history matching, model selection and production optimization. For history matching, we use the method known as ensemble smoother with multiple data assimilation. For model selection, we propose a procedure grounded on the calculation of distances defined in a metric space and a minimization procedure to determine the optimal set of representative models. For production optimization, we use the ensemble-based optimization method. We investigate the performance of each method separately before testing the complete closed-loop in a benchmark problem based on Namorado field in Campos Basis, Brazil. The results showed the effectiveness of the proposed methods to form a robust closed-loop workflow. [ABSTRACT FROM AUTHOR]

Multi-Layer Capacitance-Resistive Models (ML-CRMs), unlike the Capacitance-Resistive Model (CRM), can be applied for water flooding performance prediction in a layered reservoir. The ensemble Kalman filter (EnKF) method is applied to match the parameters in ML-CRMs based on a historical production process for each layer. A methodology for maximizing production and financial gain is presented that bases the ensemble-based optimization (EnOpt) method upon the ML-CRMs as the underlying dynamical system. This allows the utilization of historical observation data to characterize and predict the layered reservoir response and further use them to control the injection and production wells to maximize financial gain from the reservoir. The EnOpt method enables the incorporation of nonlinear effects in the ML-CRMs description. Combining the EnOpt method with ML-CRMs (an approximate model), rather than reservoir simulations, is computationally efficient. This can be helpful in cases with scarce geological data or a reservoir that involves a large number of active wells. Synthetic examples of a layered reservoir are performed to validate how EnOpt/ML-CRMs can successfully match observation data with good forecasting and optimization ability. [ABSTRACT FROM AUTHOR]

In this study, an integrated optimization procedure was proposed for well performance analysis in a heterogeneous oil field from southwest Iran. Proposed integrated strategy optimizes well performance in an iterative manner, while fluid properties, geological and petrophysical data were analyzed separately by new advanced methods. Subsequently, integrated solution was introduced by integrating various well types geometry and fine zonation of the target formation. Combination between each zone and each well type was based on three potential oil bearing zones predefined by well log data analysis. Target formation, the Sarvak formation limestone, was divided into 10 different zones and much more subzones according to the geological and petrophysical data. Afterwards, 17 wells with various geometries were modeled in the upscaled reservoir model. Fluid properties and reservoir characteristics were also obtained and analyzed from different samples taken from target formation. Results of applying the proposed integration strategy defined that the maximum production rate occurs while water cut does not exceed 10%. Performed analysis also suggest 15 MPa as the optimal value of the surface gas lift pressure and state that the gas oil ratio should not exceed this ratio at the flowing wells bottom condition. Sensitivity analysis on tube size expresses 4.67-inches tube as the optimum value. Meanwhile, sensitivity analysis on flow rate represents that optimal value of this parameter shows high sensitivity to changes in production index. Results showed that 7% change in production index will make 4% changes in optimal gas flow rate value. [ABSTRACT FROM AUTHOR]

In order to optimize the quality-related costs, the quality assurance within the production must be designed in terms of economical criteria. This design is time-consuming and cost-intensive. However, due to the increasing individualization up to lot size one, the quality assurance must be adapted in increasingly shorter cycles in order to achieve an economical optimal quality assurance at any time. The realization of an adaptive quality assurance within the production enables manufacturing companies to achieve a minimum of quality-related costs at any time despite an increasing individualization up to lot size one. Due to their high degree of swiftness regarding data acquisition, data processing and output of data in real-time, and furthermore, their capability to control physical elements with computer-based algorithms in an intertwined way, cyber-physical systems (CPS) are predestined to perform an adaptive quality assurance within the production. But, no approach towards an adaptive quality assurance, which is performed by a cyber-physical system in order to achieve a minimum of quality-related costs at any time despite an increasing individualization of manufactured products up to lot size one, has been described in literature yet. This paper fills the gap by showing an approach towards an adaptive quality assurance within the production, which is performed by a cyber-physical system. [ABSTRACT FROM AUTHOR]

Ureases are nickel-dependent enzymes that catalyze the hydrolysis of urea to ammonia and carbon dioxide. In soybean ( Glycine max ), the embryo-specific urease (eSBU), the ubiquitous urease (uSBU), and a third isoform (SBU-III) are synthesized. Our group has previously demonstrated that eSBU, purified from seeds, has antifungal properties against phytopathogenic fungi, entomotoxicity against Dysdercus peruvianus , the ability to induce blood platelet aggregation, and these properties are independent of its enzymatic activity. Here we describe the biological properties of apo-uSBU fused to glutathione S-transferase (GST) produced in Escherichia coli . Removal of GST affected apo-uSBU stability. We performed a Response Surface Methodology to optimize GST-uSBU production to 5 mg per liter and then bioassays were carried out. The recombinant protein exhibited inhibitory effects on filamentous fungi and affected fungal secondary metabolism. Candida albicans and C. tropicalis were also susceptible to GST-uSBU and formed pseudo-hyphae. The fusion protein was toxic against Rhodnius prolixus, with the toxicity being accompanied by in vivo and in vitro hemocyte aggregation. Rabbit platelet also aggregated in the presence of GST-uSBU. Thus, uSBU displayed similar biological properties as previously described for eSBU even when fused to GST, reinforcing the proposed role of ureases in plant defense. [ABSTRACT FROM AUTHOR]

Today, remanufacturing is a key industrial discipline at the end of a product's life cycle. Unfortunately, there is a lack of knowledge when it comes to the assessment of remanufacturing processes (technical and organizational processes within the production) and the comparability of remanufacturing processes. To close the lack of knowledge, both in industry and research, this paper shows the main aspects of a holistic approach to assess remanufacturing processes. The rudiments of the assessment approach are a maturity model, the value stream methodology and material flow simulations. On the one hand, the holistic approach will enable remanufacturing companies to analyze their processes, and on the other hand, the approach will enable them to manage the progress of improvement of their remanufacturing processes. At the end of the day, the holistic approach will support the remanufacturing industry to improve their competitiveness by increasing their cost and resource efficiency. [ABSTRACT FROM AUTHOR]

The optimization of subsurface flow processes under geological uncertainty technically requires flow simulation to be performed over a large set of geological realizations for each function evaluation at every iteration of the optimizer. Because flow simulation over many permeability realizations (only permeability is considered to be uncertain in this study) may entail excessive computation, simulations are often performed for only a subset of ‘representative' realizations. It is however challenging to identify a representative subset that provides flow statistics in close agreement with those from the full set, especially when the decision parameters (e.g., time-varying well pressures, well locations) are unknown a priori, as they are in optimization problems. In this work, we introduce a general framework, based on clustering, for selecting a representative subset of realizations for use in simulations involving ‘new’ sets of decision parameters. Prior to clustering, each realization is represented by a low-dimensional feature vector that contains a combination of permeability-based and flow-based quantities. Calculation of flow-based features requires the specification of a (base) flow problem and simulation over the full set of realizations. Permeability information is captured concisely through use of principal component analysis. By computing the difference between the flow response for the subset and the full set, we quantify the performance of various realization-selection methods. The impact of different weightings for flow and permeability information in the cluster-based selection procedure is assessed for a range of examples involving different types of decision parameters. These decision parameters are generated either randomly, in a manner that is consistent with the solutions proposed in global stochastic optimization procedures such as GA and PSO, or through perturbation around a base case, consistent with the solutions considered in pattern search optimization. We find that flow-based clustering is preferable for problems involving new well settings (e.g., time-varying well bottom-hole pressures) or small changes in well configuration, while both permeability-based and flow-based clustering provide similar results for (new) random multiwell configurations. We also investigate the use of efficient tracer-type simulations for obtaining flow-based features and demonstrate that this treatment performs nearly as well as full-physics simulations for the cases considered. The various procedures are applied to select realizations for use in production optimization under uncertainty, which greatly accelerates the optimization computations. Optimization performance is shown to be consistent with the realization-selection results for cases involving new decision parameters. [ABSTRACT FROM AUTHOR]

In the oil and gas industry, reservoir simulation provides reliable information regarding processes which occur in the interior of a reservoir during oil or gas field development. And production optimization could be achieved through a robust reservoir simulation program. In this paper, a new method based on an augmented Lagrangian function and the Stochastic Gradient and the Finite Difference (SGFD) for nonlinear constrained production optimization is proposed. The method can accelerate computation and improve accuracy of the approximate gradient. The approximate gradient is very close to the true gradient and shows a better convergence compared to the Gaussian distribution Simultaneous Perturbation Stochastic Approximation (GPSA) method. Through the test of a real reservoir, the injection and production rates have been constrained successfully, which enhances oil or gas recovery and offers the best net present value (NPV). This method also offers theoretical support for the smart field system. [ABSTRACT FROM AUTHOR]

We propose to solve a black-oil reservoir optimal control problem with the Direct Multiple Shooting Method (MS). MS allows for parallelization of the simulation time and the handling of output constraints. However, it requires continuity constraints on state variables to couple simulation intervals. The black-oil fluid model, considering volatile oil or wet gas, requires a change of primary variables for simulation. This is a consequence of the absence of a fluid phase due to dissolution or vaporization. Therefore, reservoir simulators parametrize the states with an augmented vector and select primary variables accordingly. However, the augmented state vector and the corresponding change of primary variables are not suitable for the application of MS because the optimization problem formulation must change according to the change of variables. Thus, we propose a minimal state-space variable representation that prevents this shortcoming. We show that there is a bijective mapping between the proposed state-space representation and the augmented state-space. The minimal representation is used for optimization and the augmented representation for simulation, thereby keeping the simulator implementation unchanged. Therefore, the proposed solution is not invasive. Finally, the application of the method is exemplified with benchmark cases involving live oil or wet gas. Both examples emphasize the requirement of output constraints which are efficiently dealt with the MS method. [ABSTRACT FROM AUTHOR]

Production optimization of a water flooding project aims to determine the optimum production strategy for maintaining the reservoir under real-time optimal controls, and maximizing the oil production or net present value (NPV) of the project. Current production optimization method with a conventional grid-based simulator bears a high computational cost. Also, cumbersome history matching procedures are often required to build a reliable reservoir model for reducing the uncertainty of the optimized production strategies. To address the drawbacks of such conventional method, data-driven surrogate models, e.g., the correlation-based model, are developed to acquire interwell connectivity; such history-matched model can be then used for production optimization. It is noted that, however, the correlation-based models cannot handle the case where the conversion of duty occurs for some wells draining the reservoir, e.g., a production well is converted to a producer. Recently, we have developed a physics-based data-driven interwell numerical simulation model (INSIM) which can predict the production rates of individual phases and handle conversion-of-duty scenarios between any well pair. INSIM is derived by solving the material balance equation and augmenting the Buckley–Leverett theory. In this study, the INSIM is applied for the first time to optimize the production of water flooding projects. We derive and improve the INSIM by considering the active bottom or edge aquifers that are often encountered in real reservoirs. The improved INSIM can handle two types of aquifers: constant pressure aquifer and constant volume aquifer. By applying the inversion theory based on a finite-difference algorithm, we estimate the model parameters by history matching the historic production data. Subsequently, the history-matched INSIM is used for optimizing NPV of water flooding projects in both a synthetic reservoir and a real reservoir with an active aquifer. The injection rate of each injection well is treated as a tuning variable. As for the synthetic model, the simulation results show that the inversely derived model parameters are able to characterize the interwell formation properties, and the optimization result based on INSIM is in a good agreement with that obtained with the commercial simulator Eclipse 100. Similar history-matching results are also obtained when INSIM is applied to a real fracture-vuggy reservoir with an active aquifer. After production optimization of this real reservoir with INSIM, we achieve a 4% reduction in the water cut but a 20% increase in the oil production rate, which verifies the soundness of the proposed method. [ABSTRACT FROM AUTHOR]

The Analytical Element Method (AEM), originally developed for mathematical modeling of groundwater flow, is here applied in closed-loop waterflood simulations. The Parallel Analytical Streamline Simulator (PASS), based on AEM, enables fast time-of-flight (TOF) calculations and visualizations of sweep efficiency in homogeneous, heterogeneous and fractured reservoirs. Simulations with PASS can test the sweep efficiency for a wide range of well patterns even before field development. We assume a simple direct-line drive and various initial reservoir attributes: a homogenous base case and further explore the effects on the flood advance of zones with heterogeneous permeability and an impervious fault. For all cases, analytical streamline patterns and time-of-flight contours for the flood front (obtained with PASS) are compared to those generated via an independent method based on numerical discretization by a commercial reservoir simulator. The results are convergent and confirm that PASS can be used to determine in closed-loop simulations the well rates that will avoid the occurrence of premature water breakthrough in the production wells. Early breakthrough in the homogenous reservoir occurs for the central producers and occurs later for the peripheral producers. Real-time adjustments of the water injection rates based on closed-loop surveillance of the pressures in producer wells can redirect and control the reservoir flow such that the floodfront arrives simultaneously at all producers. For the heterogeneous reservoir, smart-field well-control for improved sweep efficiency is also visualized. However, when an impervious fault zone blocks the flow path between injector and producer wells the occurrence of premature arrival of injection water in some producers cannot be avoided. [ABSTRACT FROM AUTHOR]

A new optimization algorithm called sperm whale algorithm (SWA) is proposed to solve production optimization problems. This algorithm is based on the sperm whale's lifestyle. Like other population-based algorithms, SWA uses a population of solutions to find the optimum answer. One of the advantages of this method over others is that it uses two contradictory types of answers: it uses the worst and the best answers to reach the optimum point. The SWA algorithm was tested on 26 benchmarks and three benchmarks in several dimensions and one production optimization problem. The results and comparison of its performance with other algorithms show that SWA's performance is superior to other algorithms and it could be confidently used in optimization tasks. [ABSTRACT FROM AUTHOR]

The surge to maintain a high and sustainable gas supply causes shale-gas fields to be developed with a high number of wells and an extensive infrastructure. This may result in large and complex optimization problems in order to utilize shared infrastructure, optimize the field design, and improve the recovery of the natural-gas resource. Solving large-scale, field-wide shale-gas optimization problems put different requirements on the well and reservoir models than conventional history matching, as it is crucial that the models are low ordered, numerically efficient and adaptable to changing operating conditions. To this end, using appropriately designed proxy models is critical. In this paper, we construct a dynamic shale-well proxy model based on the first principles for gas flow in dry shale-gas reservoirs, rendering a semi-physical modeling approach between purely data-driven black-box models and detailed numerical models. To tune proxy models, we propose a parameter-estimation scheme that incorporates prefiltering of prediction errors between the proxy model and reference data. The prefiltering approach provides a means for emphasizing certain ranges of the dynamics in the model, and thus constitutes a structured approach for defining weights in the accompanying weighted least-squares problem. Further, we show how the proposed approach allows for considering the model tuning and spatial grid design as a joint problem with respect to the purpose of the proxy model. [ABSTRACT FROM AUTHOR]

Recently production optimization has gained increasing interest in the petroleum industry. The most computationally expensive part of the production optimization process is the evaluation of the objective function performed by a numerical reservoir simulator. Employing surrogate models (a.k.a. proxy models) as a substitute for the reservoir simulator is proposed for alleviating this high computational cost. In this study, a novel approach for constructing adaptive surrogate models with application in production optimization problem is proposed. A dynamic Artificial Neural Networks (ANNs) is employed as the approximation function while the training is performed using an adaptive sampling algorithm. Multi-ANNs are initially trained using a small data set generated by a space filling sequential design. Then, the state-of-the-art adaptive sampling algorithm recursively adds training points to enhance prediction accuracy of the surrogate model using minimum number of expensive objective function evaluations. Jackknifing and Cross Validation (CV) methods are used during the recursive training and network assessment stages. The developed methodology is employed to optimize production on the bench marking PUNQ-S3 reservoir model. The Genetic Algorithm (GA) is used as the optimization algorithm in this study. Computational results confirm that the developed adaptive surrogate model outperforms the conventional one-shot approach achieving greater prediction accuracy while substantially reduces the computational cost. Performance of the production optimization process is investigated when the objective function evaluations are performed using the actual reservoir model and/or the surrogate model. The results show that the proposed surrogate modeling approach by providing a fast approximation of the actual reservoir simulation model with a good accuracy enhances the whole optimization process. [ABSTRACT FROM AUTHOR]

Production optimization involving injection has successfully improved oil production by increasing recovery. This technique is now combined with information obtained from gradient-based methods for calculating the optimal flow rate or bottom-hole pressure (BHP) during life cycle optimization. It has been shown that if the objective functional is linear in the control and the only constraints are the upper and lower bounds on the control, then the problem will sometimes have bang–bang (on–off) optimal solutions. This paper discusses an optimal control approach to the life cycle optimization of water-flooding problem by calculating the optimal switching times given the optimal wells settings to be bang–bang controls. The optimal switching times are obtained using the gradient obtained from the adjoint-based method combined with the line search algorithm. Two numerical examples that use realistic reservoir settings are included to illustrate the application of the proposed method. [ABSTRACT FROM AUTHOR]

Grape processing industry waste was used as a novel precursor for the production of a low-cost activated carbon with zinc chloride chemical activation. The effects of the production variables, such as impregnation ratio, activation temperature and activation time on the some textural properties were investigated. The optimal activated carbon which had the highest surface area and pore volume was obtained by these conditions as follows: 6:1 impregnation ratio, 600 °C activation temperature and 60 min activation time. The optimum conditions resulted in an activated carbon with a carbon content of 81.76% and a yield of 44.13%, while the surface area of 1455 m 2 /g, with the total pore volume of 2.318 cm 3 /g, mesoporosity of 94.61% and average pore diameter of 6.81 nm. These results showed that the grape wastes seemed to be an alternative precursor for the commercial activated carbon productions. The adsorptive property was tested using Methylene blue and Metanil yellow dyes. The adsorption behaviors for both dyes were well described by the Langmuir isotherm model, showing a monolayer adsorption capacity of 417 mg/g and 386 mg/g for Methylene blue and Metanil yellow, respectively. This study demonstrated that produced optimal activated carbon could be employed as an efficient cleaner to compare with the commercial ones for the removal cationic and anionic dyes from wastewater. [ABSTRACT FROM AUTHOR]

The optimal eco-efficient design of production processes guarantees sustainable and resource-efficient manufacturing systems. Within the scope of the European project “DAPhNE”, Fraunhofer IPA enables eco-efficient design with a new integrated system for the simulation and management of energy-intensive production systems as well as related key performance indicators (KPIs). The approach presented in this paper is based on a holistic production model and simulation system including dynamic KPIs with the focus on environmental, operational and economic aspects. A main objective of the developed system is to support the engineering and optimization phases to develop and maintain eco-efficient production systems. [ABSTRACT FROM AUTHOR]

Optimal well placement and optimal well control are two important areas of study in oilfield development. Although the two problems differ in several respects, both are important considerations in optimizing total oilfield production, and so recent work in the field has considered the problem of addressing both problems jointly. Two general approaches to addressing the joint problem are a simultaneous approach, where all parameters are optimized at the same time, or a sequential approach, where a distinction between placement and control parameters is maintained by separating the optimization problem into two (or more) stages, some of which consider only a subset of the total number of variables. This latter approach divides the problem into smaller ones which are easier to solve, but may not explore search space as fully as a simultaneous approach. In this paper we combine a stochastic global algorithm (particle swarm optimization) and a local search (mesh adaptive direct search) to compare several simultaneous and sequential approaches to the joint placement and control problem. In particular, we study how increasing the complexity of well models (requiring more variables to describe the well׳s location and path) affects the respective performances of the two approaches. The results of several experiments with synthetic reservoir models suggest that the sequential approaches are better able to deal with increasingly complex well parameterizations than the simultaneous approaches. [ABSTRACT FROM AUTHOR]

This paper considers the problem of steady-state real-time optimization (RTO) of large-scale processes with a common constraint for several units, for example, a shared resource. Such problems are often studied under the context of distributed optimization, where each subsystem is locally optimized for a given shadow price of the shared resource. A central coordinator is then used to coordinate the allocation of the shared resource. In traditional RTO, such a framework would require repeatedly solving the subproblems and the central problems until convergence, which can be computationally expensive. To address this issue, we use a feedback-based distributed RTO scheme based on Lagrangian decomposition, where the local subproblems and the central problems are converted into feedback control problems. That is, by appropriately choosing the controlled variables in each subproblem, the overall process can be asymptotically driven to its optimal operation using feedback controllers. In this paper, we validate this approach using a lab-scale experimental rig that emulates a subsea oil production network, where the common resource is the gas lift that must be optimally allocated among the wells. We also benchmark its performance with a numerical optimization-based RTO scheme. • Distributed Feedback-based RTO (DFRTO) is implemented on the lab scale plant. • DFRTO allows for timescale separation of the subproblems and coordinator problem. • DFRTO/Primal–Dual eliminates the need for a numerical solver. • Guidelines for DFRTO/Primal–Dual practical implementation. [ABSTRACT FROM AUTHOR]

• Sensor and PLC data enabled digital twin applications stand as a remedy to minimize material consumption, maximize product performance and prevent rework through process quality improvements in production lines by providing insights to process parameters, enabling preventive actions, and hence optimize production. • Digital twin applications require in-depth simulation expertise and an advanced understanding of both the process and the product. A digital twin can be modeled and validated after a detailed analysis of the process characteristics, process parameters, and process challenges with material modelling. • Finite element simulations and several data analytic tools can be combined to obtain the digital twin. Neo-Hookean (NH) Model is helpful while estimating temperature-dependent material definitions through WLF approximation, as it provides adequately accurate results for the simulation of the system. • The digital twin's implementation into the production line has significantly improved production KPIs and quality by decreasing the scrap ratio by 50% and the raw material consumption by 10%, resulting in a significant annual cost savings. • Digital twins can be used as enablers of production optimization with an improved sustainability performance. Collaboration and production data availability are the enablers of success. Reducing material consumption is a rising issue for manufacturers as the UN's Sustainable Development Goals and the European Union's Green New Deal minimize carbon footprints. Sensors and PLC data-enabled digital twin applications stand as a remedy to minimize material consumption, maximize product performance and prevent rework through process quality improvements. They also provide insights to process parameters, enabling preventive actions and hence optimize production. However, digital twin applications require in-depth simulation expertise and an advanced understanding of the process and the product. This paper aims to present a digital twin modeling application for improving process and product quality. Hence, the sustainable production performance in a refrigerator production line of a large Turkish durable goods manufacturer, Arçelik, collaborates with a Digital Twin Startup, Simularge. The digital twin modeling used sensors and PLC data. The development team validated the digital twin after a detailed analysis of the process characteristics, process parameters, and process challenges with material modeling. Finite element simulations and several data analytic tools were combined to obtain the digital twin. Arçelik used the digital twin of the thermoforming process in its production system for better optimization. This implementation has significantly improved production KPIs and quality by decreasing the scrap ratio by 50 % and the raw material consumption by 10 %, resulting in an annual savings of USD 2 Million. Digital twins may enable production optimization with an improved sustainability performance. Collaboration and production data availability are the enablers of success. © 1905 Elsevier Science. All rights reserved [ABSTRACT FROM AUTHOR]

Practical production optimization often involves determining a large number of well control settings such that an objective function is maximized or minimized. When derivative-free population-based optimization algorithms are applied for these high-dimensional problems, inefficient performance or slow convergence can be observed. In this work, we develop a multigroup optimization strategy to mitigate these issues. The first step of the method entails the ranking of decision variables based on their statistical significance, as observed during a short preprocessing optimization run. Variable groupings are then established. Group-by-group optimization, in which only a subset of decision variables is considered at each stage, is then conducted. As a final step, additional (limited) iterations with the full set of variables are performed. Particle swarm optimization (PSO), a global stochastic search method, is applied in this study, though the multigroup treatment is compatible with a range of algorithms. Detailed performance comparisons between multigroup strategies and standard optimization are presented for a model based on the 3D Brugge case. The problem involves 64 wells and 320 decision variables. The multigroup optimization framework includes several algorithmic parameters, and the use of different settings for these is shown to provide a range of promising procedures. Specifically, one such multigroup treatment requires 72% of the flow simulations of standard optimization, but it provides a final net present value (NPV) that is 2.6% higher than that from standard optimization. Another procedure provides essentially the same NPV as standard optimization but with only 40% of the flow simulations. The multigroup framework developed in this study can be extended to treat other challenging high-dimensional problems such as field development optimization. • Multigroup methodology developed for high-dimensional well control optimization. • Entails ranking and grouping of decision variables followed by staged optimizations. • Approach is tested on modified 3D Brugge model involving 320 decision variables. • Methodology provides a set of procedures with advantages over standard optimization. • Multigroup treatment provides same NPV as standard method with 60% fewer runs. [ABSTRACT FROM AUTHOR]

Chromate reductase production by a bacterial isolate CSB 9 has been optimized to a high titer in the present investigation. The bacterium isolated previously from a chromium contaminated site and identified as Bacillus amyloliquefaciens following biochemical and 16S rRNA gene sequencing, was evaluated for chromate reductase production. Chromate reductase activity of B. amyloliquefaciens (CSB 9) was associated with the contribution of extracellular enzymes under the influence of various physico-chemical parameters. In presence of 50 µM of Cr(VI) concentration, the bacterial growth exhibited 8 h of lag phase, 8–32 h of log phase and stationary phase upto 48 h. The maximum chromate reductase production was achieved in the presence of 50 µM of Cr(VI); 0.5% (w/v) of glucose; 1% (w/v) of trypton; 0.5% (w/v) of Na 2 HPO 4 ; 0.5% (w/v) KNO 3 ; 0.5% (w/v) KCl and 0.5% (w/v) thiamine as source of vitamin with the initial medium pH of 7.0 and incubation at 35 °C under shaking condition (100 rpm). In optimized conditions, the chromate reductase production was enhanced to a significant level of 3.67±0.014 U ml −1 as compared to unoptimized condition (2.37±0.029 U ml −1 ) with decrease in time from 20 h (late log phase) under unoptimized condition to 16 h under optimized condition. This communication elucidates the enhanced production of extracellular chromate reductase from a newly isolated bacterium B. amyloliquefaciens from chromite mine soil under optimized conditions which can be potentially exploited for biotechnological application. [ABSTRACT FROM AUTHOR]

Abstract: The development of a gas field requires accurate planning, but the gas production rate is one of the main challenges in determining the feasibility of a gas project. An optimum gas production rate is determined not only by the gas reserve and reservoir characteristics but also by the consumer's requirements of the sales gas pressure, duration of the gas sales contract and gas price. This paper presents a gas production optimization model based on the marginal cost approach to maximize economic profit using a case study in the Donggi gas field. The results reveal that increasing the sales gas pressure and gas price raises the optimum gas production rate and increases the maximum profit; meanwhile, increasing the duration of a gas sales contract will reduce the optimum gas production rate and reduce or increase the maximum profit depending on the gas reserve and reservoir characteristics. This work clearly shows the relationship between the user's requirements and optimum gas production rate, which is an important piece of information for negotiating the gas price and planning production. [Copyright &y& Elsevier]

In this study, we aimed to convert capsicum (Capsicum annuum L.) industrial processing pulp (CP) into low-cost and high-quality activated carbon (AC) under optimal production conditions through single-step KOH-catalyzed pyrolysis. Optimal production conditions were determined by investigating the effects on some textural characteristics of the produced ACs by changing the process variables such as mixing ratio (KOH/CP, w/w), pyrolysis temperature, and pyrolysis duration, respectively. The AC (CPAC) with the BET (Brunauer-Emmett-Teller) highest surface area (1564 m2/g), pore-volume (0.623 cm3/g), and average pore size (1.8 nm) was produced under conditions with a mixing ratio of 1.5, a pyrolysis temperature of 700 °C, and a duration of 1 h. It was characterized by nitrogen adsorption/desorption, Scanning Electron Microscopy (SEM)/Energy Dispersive X-Ray (EDX), X-ray Diffraction (XRD), Raman Microscopy and Fourier Transform Infrared Spectroscopy (FT-IR) techniques. In addition, the adsorption ability was determined by examining the removal performance of water pollutants such as methylene blue (MB) and methanol yellow (MY) dyes, 2,4-dichlorophenoxyacetic acid herbicide (2,4-D), diclofenac sodium drug (DCF), copper(II) ion (Cu(II)), and nitrate (NO 3 −), which were chosen because they are frequently detected in industrial wastewater, and found to be 294.1, 370.4, 384.6, 294.1, 212.8 and 59.5 mg/g, respectively. The supercapacitor performance of the electrodes developed from CPAC was tested as the coin size cell. The gravimetric capacitances of the electrodes were calculated as 198 F/g at the current densities of 0.5 A/g. The results of this study reveal that CPAC can be used as an alternative to commercial carbon as a clean and ecologically compatible low-cost (~$1.75 per kg) adsorbent that can improve water quality, and promising electrode material for low-cost and high-performance carbon-based supercapacitors. [Display omitted] • CP was converted into a high surface area and supermicroporous activated carbon. • Optimal activated carbon (CPAC) production conditions were determined. • The mixing ratio has a significant effect on the pore structure. • CPAC has high adsorptive properties in removing water pollutants. • CPAC-derived electrodes showed high electrochemical performance [ABSTRACT FROM AUTHOR]

Abstract: Typically, a well is produced with a constant oil rate with constant gas–oil ratio (GOR) during the subcritical phase, i.e. before gas breakthrough. The presence of gas coning in production wells may reduce the oil production. The decline in the oil rate will be followed by an increase in the well head pressure. From an economical and operational point of view, this condition may be undesirable for several reasons; the gas price is much lower than the oil price, the afflicted well may be abandoned early, and the gas handling capacity often is a constraint. Therefore, there is an incentive to produce such wells in their subcritical phase for an extended period of time. In this paper, the gas coning process in a gas–oil reservoir completed with a single horizontal well is analytically modeled, simulated, and analyzed applying a nonlinear control approach. The horizontal well model which describes the interaction between the well and the reservoir may be cast into a boundary control problem of the porous media equation with two boundary conditions; a homogeneous Neumann's boundary condition describing no-flow at the outer boundary of the reservoir, and a nonlinear boundary condition describing the well production rate. A well rate controller for the boundary control problem is designed using the backstepping method. The controller holds some formal performance guarantees and requires information on the gas–oil contact (GOC) at the well heel only. Furthermore, the controller has a tuning parameter which can be used to maximize a suitable performance measure, e.g. the net present value (NPV). The controller is evaluated using a detailed Eclipse simulator of a gas coning reservoir. Simulation results show significant improvement of production profit of the proposed method compared to a conventional method which usually uses a constant rate until gas breakthrough. [Copyright &y& Elsevier]

Abstract: The topic of this paper is the application of nonlinear model predictive control (NMPC) for optimizing control of an offshore oil and gas production facility. Of particular interest is the use of NMPC for direct short-term production optimization, where two methods for (one-layer) production optimization in NMPC are investigated. The first method is the unreachable setpoints method where an unreachable setpoint is used in order to maximize oil production. The ideas from this method are combined with the exact penalty function for soft constraints in a second method, named infeasible soft-constraints. Both methods can be implemented within standard NMPC software tools. The case-study first looks into the use of NMPC for ‘conventional’ pressure control, where disturbance rejection of time-varying disturbances (caused, e.g., by the ‘slugging’ phenomenon) is an issue. Then the above two methods for production optimization are employed, where both methods find the economically optimal operating point. Two different types of reservoir models are studied, using rate-independent and rate-dependent gas/oil ratios. These models lead to different types of optimums. The relative merits of the two methods for production optimization, and advantages of the two one-layer approaches compared to a two-layer structure, are discussed. [Copyright &y& Elsevier]

Abstract: Anoxybacillus beppuensis TSSC-1 (GenBank Number, EU710556), a thermophilic bacterium isolated from a hot spring reservoir, was found to optimally secrete a monomeric α-amylase at 55°C and pH 7. The enzyme was purified to homogeneity by a single-step purification on phenyl sepharose 6FF, achieving a 58% yield, 10,000U/mg specific activity and 19.5 fold purification. The molecular weight, K m and V max were 43kD, 0.5mgml−1 and 3571.42μmolml−1 m−1, respectively. The enzymatic catalysis of soluble starch was optimum at 80°C and pH 7. The thermodynamic parameters, K d, t 1/2, ΔH*, ΔS*, E and ΔG*, were consistent. The very compact structure of the enzyme and the transitional enzyme–substrate complex resisted denaturation at extreme temperatures and alkaline pH. The K d and t 1/2 measurements were consistent with the high thermostability and pH tolerance observed. The structural stability of the enzyme was also reflected by the values of ΔH*, ΔS*, E and ΔG*. While the enzyme did not exhibit metal ion dependency, it was resistant to chemical denaturation. The broad thermo- and pH-tolerance of this enzyme suggests potential commercial opportunities. [Copyright &y& Elsevier]