Evaluation and selection of the best artificial lift method for optimal production using PIPESIM software.

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.

Suppression of toxic transgene expression by optimized artificial miRNAs increases AAV vector yields in HEK-293 cells.

Adeno-associated virus (AAV) vectors have become the leading platform for gene delivery in both preclinical research and therapeutic applications, making the production of high-titer AAV preparations essential. To date, most AAV-based studies use constitutive promoters (e.g., CMV, CAG), which are also active in human embryonic kidney (HEK)-293 producer cells, thus leading to the expression of the transgene already during production. Depending on the transgene's function, this might negatively impact producer cell performance and result in decreased AAV vector yields. Here, we evaluated a panel of diverse microRNA (miRNA)-based shRNA designs to identify a highly potent artificial miRNA for the transient suppression of transgenes during AAV production. Our results demonstrate that insertion of miRNA target sites into the 3' UTR of the transgene and simultaneous expression of the corresponding miRNA from the 3' UTR of conventional AAV production plasmids (rep/cap, pHelper) enabled efficient silencing of toxic transgene expression, thereby increasing AAV vector yields up to 240-fold. This strategy not only allows to maintain the traditional triple-transfection protocol, but also represents a universally applicable approach to suppress toxic transgenes, thereby boosting vector yields with so far unprecedented efficiency.

An optimization model for monthly time-step drilling schedule under planned field production.

The field production profile over the yearly horizon is planned for a balance between economy, security, and sustainability of energy. An optimal drilling schedule is required to achieve the planned production profile with minimized drilling frequency and summation. In this study, we treat each possible production process of each well as a dependent time series and the basic unit. Then we ensemble all of them into a tensor. Based on formulated tensor calculation and Lasso regularization, a linear mathematical optimization model for well drilling schedule was developed. The model is aimed at minimizing production profile error while optimizing drilling frequency and summation. Although the model proposed in this work requires more memory consumption to be solved using a computer, it is assured as a linear model and could be numerically globally solved in a stable and efficient way using gradient descent, avoiding complex nonlinear programming problems. Main input data and parameters involved in the model are analyzed in detail to understand the effects of different production parameters on the drilling schedule and production profile. The proposed model in this work can evaluate the manual drilling schedule and automatically generate an optimized drilling schedule for the gas field, significantly reducing development plan formulation time.

A Crisscross-Strategy-Boosted Water Flow Optimizer for Global Optimization and Oil Reservoir Production.

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.

Progress and Prospects of Natural Glycoside Sweetener Biosynthesis: A Review.

To achieve an adequate sense of sweetness with a healthy low-sugar diet, it is necessary to explore and produce sugar alternatives. Recently, glycoside sweeteners and their biosynthetic approaches have attracted the attention of researchers. In this review, we first outlined the synthetic pathways of glycoside sweeteners, including the key enzymes and rate-limiting steps. Next, we reviewed the progress in engineered microorganisms producing glycoside sweeteners, including de novo synthesis, whole-cell catalysis synthesis, and in vitro synthesis. The applications of metabolic engineering strategies, such as cofactor engineering and enzyme modification, in the optimization of glycoside sweetener biosynthesis were summarized. Finally, the prospects of combining enzyme engineering and machine learning strategies to enhance the production of glycoside sweeteners were discussed. This review provides a perspective on synthesizing glycoside sweeteners in microbial cells, theoretically guiding the bioproduction of glycoside sweeteners.

Improvement and enhancement of oligosaccharide production from Lactobacillus acidophilus using statistical experimental designs and its inhibitory effect on colon cancer.

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 BCL2 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.

A reinforcement learning method for optimal control of oil well production using cropped well group samples.

The influence of geological development factors such as reservoir heterogeneity needs to be comprehensively considered in the determination of oil well production control strategy. In the past, many optimization algorithms are introduced and coupled with numerical simulation for well control problems. However, these methods require a large number of simulations, and the experience of these simulations is not preserved by the algorithm. For each new reservoir, the optimization algorithm needs to start over again. To address the above problems, two reinforcement learning methods are introduced in this research. A personalized Deep Q-Network (DQN) algorithm and a personalized Soft Actor-Critic (SAC)algorithm are designed for optimal control determination of oil wells. The inputs of the algorithms are matrix of reservoir properties, including reservoir saturation, permeability, etc., which can be treated as images. The output is the oil well production strategy. A series of samples are cut from two different reservoirs to form a dataset. Each sample is a square area that takes an oil well at the center, with different permeability and saturation distribution, and different oil-water well patterns. Moreover, all samples are expanded by using image enhancement technology to further increase the number of samples and improve the coverage of the samples to the reservoir conditions. During the training process, two training strategies are investigated for each personalized algorithm. The second strategy uses 4 times more samples than the first strategy. At last, a new set of samples is designed to verify the model's accuracy and generalization ability. Results show that both the trained DQN and SAC models can learn and store historical experience, and push appropriate control strategies based on reservoir characteristics of new oil wells. The agreement between the optimal control strategy obtained by both algorithms and the global optimal strategy obtained by the exhaustive method is more than 95%. The personalized SAC algorithm shows better performance compared to the personalized DQN algorithm. Compared to the traditional Particle Swarm Optimization (PSO), the personalized models were faster and better at capturing complex patterns and adapting to different geological conditions, making them effective for real-time decision-making and optimizing oil well production strategies. Since a large amount of historical experience has been learned and stored in the algorithm, the proposed method requires only 1 simulation for a new oil well control optimization problem, which showing the superiority in computational efficiency.

Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises.

In the industrial sector, production processes are continuously evolving, but issues and delays in production are still commonplace. Complex problems often require input from production managers or experts even though Industry 4.0 provides advanced technological solutions. Small and medium-sized enterprises (SMEs) normally rely more on expert opinion since they face difficulties implementing the newest and most advanced Industry 4.0 technologies. This reliance on human expertise can cause delays in the production processes, ultimately, impacting the efficiency and profitability of the enterprise. As SMEs are mostly niche markets and produce small batches, dynamics in production operations and the need for quick responses cannot be avoided. To address these issues, a decision support method for dynamic production planning (DSM DPP) was developed to optimize the production processes. This method involves the use of algorithms and programming in Matlab to create a decision support module that provides solutions to complex problems in real-time. The aim of this method is to combine not only technical but also human factors to efficiently optimize dynamic production planning. It is hardly noticeable in other methods the involvement of human factors such as skills of operations, speed of working, or salary size. The method itself is based on real-time data so examples of the required I 4.0 technologies for production sites are described in this article-Industrial Internet of Things, blockchains, sensors, etc. Each technology is presented with examples of usage and the requirement for it. Moreover, to confirm the effectiveness of this method, tests were made with real data that were acquired from a metal processing company in Lithuania. The method was tested with existing production orders, and found to be universal, making it adaptable to different production settings. This study presents a practical solution to complex problems in industrial settings and demonstrates the potential for DSM DPP to improve production processes while checking the latest data from production sites that are conducted through cloud systems, sensors, IoT, etc. The implementation of this method in SMEs could result in significant improvements in production efficiency, ultimately, leading to increased profitability.

Simulation and optimization of scrap wagon dismantling system based on Plant Simulation.

Based on the existing plant layout and process flow, a simulation analysis was conducted using the Plant Simulation platform with the utilization efficiency of each station and production capacity of the dismantling system as indicators. A problem with long-term suspension in the disassembly process was determined. Based on the two optimization directions of increasing material transportation equipment and expanding the buffer capacity, a cost-oriented optimization model is established. A genetic algorithm and model simulation were used to solve the model. An optimization scheme that satisfies the production needs and has the lowest cost is proposed. The results show that the optimized dismantling system solves the suspended work problem at the dismantling station and a significant improvement in productivity and station utilization efficiency compared with the previous system.

Production optimization in the time of pandemic: an SIS-based optimal control model with protection effort and cost minimization.

The COVID-19 pandemic wreaks havoc in supply chains by reducing the production capacity of some essential suppliers, closure of production facilities or the absence of infected workers. In this paper, we present three decision support models for a plant manager to help in deciding on (a) the level of protection of the workforce against the spread of the virus in the absence of regional protection measures, (b) on the duration of the protection, and (c) the level of protection of the workforce with regional protection measures enforced by health authorities. These decision models are based on a SIS epidemiological model which takes into account the possibility that a worker can infect others but also that even when recovered can be infected again. The first and third models prescribe how, in time, the protection effort in terms of prophylactic measures must be deployed. The second model extends the first one as it also determines the length the protection effort must be deployed. The proposed models have been applied to the case of a meat processing plant that must satisfy the demand of a large-scale retailer. Clearly, to achieve production targets and satisfy customers' demand, plants in this labor-intensive industry rely on the number of healthy workers and the service level of suppliers. Our results indicate that these models provide managers with the tools to understand and measure the impact of an infection on production and the corresponding cost. Along the way, this work illustrates the ripple effect as suppliers affected by the pandemic are unable to fulfill the processing plant requirements and so the retailer's orders. Our findings provide normative guidance for supply chain decision support systems under risk of pandemic induced disruptions using a quantitative model-based approach.

On-site enriched production of cellulase enzyme using rice straw waste and its hydrolytic performance evaluation through systematic dynamic modeling.

The application of on-site produced cellulolytic enzymes in place of commercial enzymes towards hydrolytic preparations of reducing sugars using inexpensive lignocellulosic wastes is considered the most efficient strategy to accomplish a cost-effective biofuel production process. Along with improved production, intrinsic and systematic performance evaluation of the produced enzyme during the hydrolysis process through kinetic intervention remains a crucial requirement for achieving the improved performance of the process. With this motivation, the present study primarily deals with the nutritionally optimized production strategy of cellulases from rice straw (RS) waste using Trichoderma reesei (MTCC 164). The highest cellulase production was obtained 8.09 ± 0.32 g/l in batch mode at optimized combinations of 3.5% (w/v) RS inducer, 3.0% (w/v) lactose, and 1.5% (w/v) peptone. Production was further improved through pH-regulated (pH 5.5 to 6.5) fed-batch fermentations. The enzyme produced at pH 6 was considered for hydrolysis studies at 4 to 10% (w/w) solid loading due to reasonable exoglucanase, endoglucanase, and maximum ß-glucosidase activity levels of 9.3 U/ml, 3.87 U/ml, and 2.65 U/ml respectively. Multi-reaction systematic kinetic modeling was implemented to evaluate enzyme performance during hydrolysis, and the values of inhibitory kinetic parameters (K2r = 7.1 < K1r = 18.5 < K3r = 276.6) suggested that sequential conversion of cellulose to glucose by existing enzyme components was more dominant over direct conversion.

AI-Based Support System for Monitoring the Quality of a Product within Industry 4.0 Paradigm.

Three-dimensional (3D) printing, also known as additive manufacturing (AM), has already shown its potential in the fourth technological revolution (Industry 4.0), demonstrating remarkable applications in manufacturing, including of medical devices. The aim of this publication is to present the novel concept of support by artificial intelligence (AI) for quality control of AM of medical devices made of polymeric materials, based on the example of our own elbow exoskeleton. The methodology of the above-mentioned inspection process differs depending on the intended application of 3D printing as well as 3D scanning or reverse engineering. The use of artificial intelligence increases the versatility of this process, allowing it to be adapted to specific needs. This brings not only innovative scientific and technological solutions, but also a significant economic and social impact through faster operation, greater efficiency, and cost savings. The article also indicates the limitations and directions for the further development of the proposed solution.

A statistical strategy for optimizing the production of α-galactosidase by a newly isolated Aspergillus niger NRC114 and assessing its efficacy in improving soymilk properties.

BACKGROUND: α-Galactosidase is widely distributed in plants, microorganisms, and animals, and it is produced by different fungal sources. Many studies have confirmed the valuable applications of α-galactosidase enzymes for various biotechnological purposes, like the processing of soymilk. RESULTS: Aspergillus niger NRC114 was exploited to produce the extracellular α-galactosidase. One factor per time (OFT) and central composite design (CCD) approaches were applied to determine the optimum parameters and enhance the enzyme production. The CCD model choices of pH 4.73, 1.25% mannose, 0.959% meat extract, and 6-day incubation period have succeeded in obtaining 25.22 U/mL of enzyme compared to the 6.4 U/mL produced using OFT studies. Treatment of soymilk by α-galactosidase caused an increase in total phenols and flavonoids by 27.3% and 19.9%, respectively. Antioxidant measurements revealed a significant increase in the enzyme-treated soymilk. Through HPLC analysis, the appearance of sucrose, fructose, and glucose in the enzyme-treated soymilk was detected due to the degradation of stachyose and raffinose. The main volatile compounds in raw soymilk were acids (45.04%) and aldehydes (34.25%), which showed a remarkable decrease of 7.82% and 20.03% after treatment by α-galactosidase. CONCLUSIONS: To increase α-galactosidase production, the OFT and CCD approaches were used, and CCD was found to be four times more effective than OFT. The produced enzyme proved potent enough to improve the properties of soymilk, avoiding flatulence and undesirable tastes and odors.

Strategies to Enhance Periplasmic Recombinant Protein Production Yields in Escherichia coli.

Main reasons to produce recombinant proteins in the periplasm of E. coli rather than in its cytoplasm are to -i- enable disulfide bond formation, -ii- facilitate protein isolation, -iii- control the nature of the N-terminus of the mature protein, and -iv- minimize exposure to cytoplasmic proteases. However, hampered protein targeting, translocation and folding as well as protein instability can all negatively affect periplasmic protein production yields. Strategies to enhance periplasmic protein production yields have focused on harmonizing secretory recombinant protein production rates with the capacity of the secretory apparatus by transcriptional and translational tuning, signal peptide selection and engineering, increasing the targeting, translocation and periplasmic folding capacity of the production host, preventing proteolysis, and, finally, the natural and engineered adaptation of the production host to periplasmic protein production. Here, we discuss these strategies using notable examples as a thread.

Production optimization, stability and oil emulsifying potential of biosurfactants from selected bacteria isolated from oil-contaminated sites.

Oil pollution is of increasing concern for environmental safety and the use of microbial surfactants in oil remediation has become inevitable for their efficacy and ecofriendly nature. In this work, biosurfactants of bacteria isolated from oil-contaminated soil have been characterized. Four potent biosurfactant-producing strains (SD4, SD11, SD12 and SD13) were selected from 27 isolates based on drop collapse assay and emulsification index, and identified as species belonging to Bacillus, Burkholderia, Providencia and Klebsiella, revealed from their 16S rRNA gene-based analysis. Detailed morphological and biochemical characteristics of each selected isolate were determined. Their growth conditions for maximum biosurfactant production were optimized and found quite similar among the four isolates with a pH of 3.0 and temperature 37°C after 6 or 7 days of growth on kerosene. The biosurfactants of SD4, SD11 and SD12 appeared to be glycolipids and that of SD13 a lipopeptide. Emulsification activity of most of the biosurfactants was stable at low and high temperatures (4-100°C), a wide range of pH (2-10) and salt concentrations (2-7% NaCl). Each biosurfactant showed antimicrobial activity against two or more pathogenic bacteria. The biosurfactants were well-capable of emulsifying kerosene, diesel and soya bean, and could efficiently degrade diesel.

Structural-genetic insight and optimization of protease production from a novel strain of Aeromonas veronii CMF, a gut isolate of Chrysomya megacephala.

Structural-genetic characterization of protease producing genes and enzymes from microbial sources are seldom appreciated despite having its substantial utilization in protein engineering or genetic manipulation for biotechnological applications. Aeromonas veronii CMF, a mesophilic bacterium isolated from the gut of Chrysomya megacephala, was found to exhibited significant level of protease activity. For the revelation of genetic potential in relation to protease production, whole genome of this organism was sequenced and analysed while structure-function of different protease enzyme was predicated using various in silico analysis. The 4.5 mb CMF genome was found to encompass various types of protease and mostly they are neutral in nature. Enzyme production was highest in an optimum pH and temperature of 6.0 (32.09 ± 1.015 U/ml) and 35ºC (41.65 ± 1.152 U/ml), respectively. Other culture parameters for optimum production of protease were determined to be inoculum size (1%), incubation period (72 h), shaking condition (125 rpm), carbon and nitrogen source [2% lactose (92.21 ± 3.16 U/ml) and 0.5% urea (163.62 ± 4.31 U/ml), respectively] and effect of surfactants [0.02 mg/ml Tween 80 (174.72 ± 4.48 U/ml)]. Furthermore, A. veronii CMF exhibited significant enzyme production like serine protease (15.22 ± 0.563 U/ml), aspartate protease (33.16 ± 0.762 U/ml) and collagenase (17.26 ± 0.626 U/ml). Genomic information and results of physio-biochemical assays indicate its cost-effective potential use in different enzyme-industry.

Systematical Engineering of Synthetic Yeast for Enhanced Production of Lycopene.

Synthetic biology allows the re-engineering of biological systems and promotes the development of bioengineering to a whole new level, showing great potential in biomanufacturing. Here, in order to make the heterologous lycopene biosynthesis pathway compatible with the host strain YSy 200, we evolved YSy200 using a unique Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution (SCRaMbLE) system that is built in the Sc2.0 synthetic yeast. By inducing SCRaMbLE, we successfully identified a host strain YSy201 that can be served as a suitable host to maintain the heterologous lycopene biosynthesis pathway. Then, we optimized the lycopene biosynthesis pathway and further integrated into the rDNA arrays of YSy201 to increase its copy number. In combination with culturing condition optimization, we successfully screened out the final yeast strain YSy222, which showed a 129.5-fold increase of lycopene yield in comparison with its parental strain. Our work shows that, the strategy of combining the engineering efforts on both the lycopene biosynthesis pathway and the host strain can improve the compatibility between the heterologous pathway and the host strain, which can further effectively increase the yield of the target product.

Improvement of ethanol production in fed-batch fermentation using a mixture of sugarcane juice and molasse under very high-gravity conditions.

Ethanol fermentation in very high gravity (VHG) saves energy consumption for ethanol distillation. As the technology offers high ethanol yield and low waste generation and it can be operated at low cost, it could be more efficient at an industrial scale than other ethanol production methods. This work studied ethanol production using a fed-batch bioreactor with a working volume of 1.5 L. The main objective of this research was evaluate the effects of temperature, sugar concentration, and cellular concentration using a Central Composite Design (CCD). Experimental conditions were selected using the surface response technique obtained from the CCD, and the results were validated to test the reproducibility. The following operating conditions were selected: temperature of 27.0 °C, sugar concentration 300.0 g/L, and cell concentration 15.0% (v/v). Under these conditions, after 30 h of fermentation the ethanol concentration, productivity and yield were 135.0 g/L, 4.42 g/(L·h) and 90.0%, respectively. All sugar was completely consumed.

Reducing glucoamylase usage for commercial-scale ethanol production from starch using glucoamylase expressing Saccharomyces cerevisiae.

The development of yeast that converts raw corn or cassava starch to ethanol without adding the exogenous α-amylase and/or glucoamylase would reduce the overall ethanol production cost. In this study, two copies of codon-optimized Saccharomycopsis fibuligera glucoamylase genes were integrated into the genome of the industrial Saccharomyces cerevisiae strain CCTCC M94055, and the resulting strain CIBTS1522 showed comparable basic growth characters with the parental strain. We systemically evaluated the fermentation performance of the CIBTS1522 strain using the raw corn or cassava starch at small and commercial-scale, and observed that a reduction of at least 40% of the dose of glucoamylase was possible when using the CIBTS1522 yeast under real ethanol production condition. Next, we measured the effect of the nitrogen source, the phosphorous source, metal ions, and industrial microbial enzymes on the strain's cell wet weight and ethanol content, the nitrogen source and acid protease showed a positive effect on these parameters. Finally, orthogonal tests for some other factors including urea, acid protease, inoculum size, and glucoamylase addition were conducted to further optimize the ethanol production. Taken together, the CIBTS1522 strain was identified as an ideal candidate for the bioethanol industry and a better fermentation performance could be achieved by modifying the industrial culture media and condition.

Improved phenol sequestration from aqueous solution using silver nanoparticle modified Palm Kernel Shell Activated Carbon.

Modified Palm Kernel Shell Activated Carbon (PKSAC) using silver nanoparticle (Ag-NPs-PKSAC) was investigated on phenol uptake from aqueous solution. Effects of temperature (500-700 °C), time (90-120 min), and alkaline concentration (0.1-0.5 M) were studied on the yield and methylene blue numbers for the synthesis. Effects of initial concentration (100-200 mg/L), agitation (150-250 rpm), contact time (30-120 min), and adsorbent dosage (0.15-0.25 g) were studied in a batch experiment on percentage removal of phenol. The PKS, char, PKSAC and Ag-NPs-PKSAC were characterized using BET, FTIR, SEM, and proximate analyses. The synthesis of PKSAC was optimum at 608 °C, 0.5 M KOH, and carbonization holding time of 60 min. The optimum phenol uptake was 85.64, 90.29 and 91.70% for PKSAC, Ag-NPs-PKSAC, and commercial adsorbent, respectively. The adsorption mechanism of phenol followed the Langmuir isotherm and best described as physio-sorption with pseudo-second-order kinetics. Phenol exhibits high affinity (ΔS° = 0.0079 kJ/mol K) for Ag-NPs-PKSAC with favorable adsorption (ΔG° = -1.551 kJ/mol) at high temperature due to endothermic (ΔH° = 1.072 kJ/mol) nature of the system. The result obtained in this study compared favorably with the literature.