Full Scenario Research Articles

Optimal contract design for a supply chain with information asymmetry under dual environmental responsibility constraints

Under dual constraints of green production responsibility and recycling responsibility, the optimal contract between upstream and downstream firms is crucial to achieve low-carbon development, but information asymmetry poses a great challenge to the above contract design. Based on Stackelberg game theory, this paper develops a contract model in a supply chain with information asymmetry under dual environmental responsibility constraints, in which the producer invests in green technology while the retailer exerts green marketing efforts. The paper aims to explore how the producer designs optimal contracts under the different information structures to improve environmental performance while ensuring profitability. Results indicate that: (i) in the full information scenario, the two-part optimal contract makes the decentralized supply chain to achieve the same channel profit and carbon emissions as the centralized scenario. (ii) In the information asymmetry scenario, the producer should design differentiated contract configurations for different types of retailers to eliminate double marginalization. That is, the producer designs low wholesale price and high side payment for the high-type retailer, which enables the producer to earn high profit with low carbon emissions and makes the retailer to obtain additional information profit. While under some conditions, the producer considers the possibility of terminating cooperation with the retailer of low-type, as such a cutoff policy not only guarantees the profitability of the channel members but also improves environmental performance. Furthermore, the impact of some key factors on firms’ decisions, profits, and equilibrium carbon emissions is summarized to guide management decisions.

Core transport modelling of the DTT full power scenario using different fuelling strategies

A theory-based integrated modelling work of plasma response to deuterium fuelling in the new Divertor Tokamak Test facility (DTT) is performed, using the 1.5D transport code JETTO with the quasi-linear anomalous transport model QuaLiKiz for the core region. The full power DTT scenario E1 is investigated. It is characterised by 28.8 MW of Electron Cyclotron Resonance Heating, 10 MW of Neutral Beam Injection and 6 MW of Ion Cyclotron Resonance Heating to the plasma. Plasma density and temperature profile evolution is calculated up to the separatrix using two different fuelling methods, gas puffing and pellet injection, and two different seeding gases, argon and neon. To sustain the desired pedestal density level with gas puffing a big amount of neutral flux at the separatrix is needed. The feasibility limits of the pumping system are exceeded, regardless of the type of impurity introduced, thus making the use of pellets mandatory. The simulations performed with pellet injection as fuelling method predict that the pedestal density is well sustained with realistic parameters foreseen for the DTT pellet injector. Strong dependence of the core density on the electron cyclotron resonance (ECR) power deposition profile is found. Trapped Electron Modes dominance, low outward flux and strongly hollow density in the inner core region are foreseen with central peaked ECR power deposition profile. Ion Temperature Gradient modes dominance, inward flux and robust density sustainment on the whole radial interval are predicted for spread ECR power deposition, though with central density close to the ECR cut-off limit and with peaked impurity densities. An intermediate deposition extension is found to sustain the whole density profile and to obtain flatter core densities, as previously predicted for the reference full power DTT scenario by fixed pedestal simulations. The ECR deposition is negligibly modified by refraction changes both during a single pellet cycle and after several pellet cycles, indicating full compatibility between the ECR system and the pellet injection system.

Hemoadsorption Using CytoSorb® in Patients with Infective Endocarditis: A German-Based Budget Impact Analysis.

A considerable number of infective endocarditis (IE) patients require cardiac surgery with an increased risk for postoperative sepsis. Intraoperative hemoadsorption may diminish the risk of postoperative hyperinflammation with potential economic implications for intensive care unit (ICU) occupation. The present study aimed to theoretically investigate the budget impact of a reduced length of ICU stay in IE patients treated with intraoperative hemoadsorption in the German healthcare system. Data on ICU occupation were extrapolated from a retrospective study on IE patients treated with hemoadsorption. An Excel-based budget impact model was developed to simulate the patient course over the ICU stay. A base-case scenario without therapy reimbursement and a scenario with full therapy reimbursement were explored. The annual eligible German IE patient population was derived from official German Diagnostic-Related Group (DRG) volume data. One-way deterministic sensitivity analysis and multivariate analysis were performed to evaluate the uncertainty over the model results. The use of intraoperative hemoadsorption resulted in EUR 2298 being saved per patient in the base-case scenario without therapy reimbursement. The savings increased to EUR 3804 per patient in the case of full device-specific reimbursement. Deterministic and probabilistic sensitivity analyses confirmed the robustness of savings, with a probability of savings of 87% and 99% in the base-case and full reimbursement scenario, respectively. Intraoperative hemoadsorption in IE patients might have relevant economic benefits related to reduced ICU stays, resulting in improved resource use. Further evaluations in larger prospective cohorts are warranted.

Disruption of serotonin biosynthesis increases resistance to striped stem borer without changing innate defense response in rice.

Striped stem borer (SSB) is one of the most damaging pests in rice production worldwide. Previously, we preliminarily demonstrated that indica rice Jiazhe LM, an OsT5H (encoding tryptamine-5-hydroxylase) knockout mutant deficient in serotonin, had increased resistance to SSB as compared with its wildtype parent Jiazhe B. However, the full scenario of SSB resistance and the underlying mechanism remain unknown. In this study, we first demonstrated that the OsT5H knockout could generally increase rice resistance to SSB and then proved that the OsT5H knockout does not disrupt the innate defense response of rice plants to SSB infestation, that is, OsT5H knockout mutations neither had significant effect on the transcriptional response of defense genes upon SSB infestation, nor the profile of defense related metabolites and plant hormones, such as lignin, salicylic acid, jasmonic acid, and abscisic acid, nor the activity of reactive oxygen species(ROS) scavenging enzymes and the ROS contents. We then demonstrated that supplementation of serotonin promoted SSB growth and performance in artificial diet feeding experiments. We observed that SSB larvae feeding on Jiazhe B had serotonin 1.72- to 2.30-fold that of those feeding on Jiazhe LM at the whole body level, and more than 3.31 and 1.84 times in the hemolymph and head, respectively. Further studies showed that the expression of genes involved in serotonin biosynthesis and transport was ~88.1% greater in SSB larvae feeding on Jiahze LM than those feeding on Jiazhe B. These observations indicated that SSB increases serotonin synthesis when feeding on serotonin deficient rice but is unable to fully compensate the dietary serotonin deficiency. Put together, the present study strongly suggests that it is the deficiency of serotonin, not the secondary effect of OsT5H knockout on innate defense response confers the SSB resistance in rice, which implies that reducing serotonin level, particularly through inhibition of its inductive synthesis upon SSB damage, could be an efficient strategy for breeding SSB resistant varieties.

Linguistic neutrosophic matrix energy and its application in multiple criteria group decision-making

Matrix energy is an important representation tool of collective information. Then, it is not applied to various fuzzy and linguistic environments. To compensate for this gap, this article aims to extend the matrix energy to propose the energy of a linguistic neutrosophic matrix (LNM) for solving a multiple criteria group decision-making (MCGDM) problem, which fully contains LNMs of decision-maker weights, criteria weights, and alternative evaluations. To realize the objective, this study first presents the energy of LNM in view of the true matrix energy, the false matrix energy, and the indeterminate matrix energy. Then, a MCGDM technique is established in view of the LNM energy method in a LNM circumstance. Finally, the developed MCGDM technique using the LNM energy is used to solve the hospital location choice problem in the full LNM scenario. Meanwhile, the decision results indicate the validity and usability of the established MCGDM technique.

Analysis of Control Element Assembly Withdrawal at Full Power Accident Scenario Using a Hybrid Conservative and BEPU Approach

Reactivity Initiated Accident (RIA) scenarios require special attention using advanced simulation techniques due to their complexity and importance for nuclear power plant (NPP) safety. While the conservative approach has traditionally been used for safety analysis, it may lead to unrealistic results which calls for the use of best estimate plus uncertainty (BEPU) approach, especially with the current advances in computational power which makes the BEPU analysis feasible. In this work an Uncontrolled Control Element Assembly (CEA) Withdrawal at Full Power accident scenario is analyzed using the BEPU approach by loosely coupling the thermal hydraulics best-estimate system code (RELAP5/SCDAPSIM/MOD3.4) to the statistical analysis software (DAKOTA) using a Python interface. Results from the BEPU analysis indicate that a realistic treatment of the accident scenario yields a larger safety margin and is therefore encouraged for accident analysis as it may enable more economic and flexible operation.

A Venus in the making? Predictions for JWST observations of the ultracool M-dwarf planet LP 890-9 c

ABSTRACT The recently discovered transiting super-Earth LP 890-9 c is potentially one of the best rocky exoplanets for atmospheric characterization. Orbiting an ultracool M-dwarf at the inner edge of the habitable zone, LP 890-9 c offers a new opportunity to study the climate of rocky planets at the inner edge of the habitable zone. We investigate the molecular detectability with simulated JWST transmission spectra for five potential atmospheres of LP 890-9 c. We find that a small three-transit JWST program can infer evidence of ${\rm {H}_{2}\rm {O}}$ (at 3σ confidence) for a full runaway greenhouse scenario. Alternatively, $\rm {CO_{2}}$-dominated atmospheres resembling Venus without high-altitude terminator clouds can be identified with eight transits. However, these predictions could be complicated by the impact of clouds and/or unocculted starspots. Nevertheless, JWST observations of LP 890-9 c could provide critical insights and potentially distinguish between models of rocky planets at the inner edge of the habitable zone.

Assessment of data intelligence algorithms in modeling daily reference evapotranspiration under input data limitation scenarios in semi-arid climatic condition.

Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), XGBoost Ensemble Tree, Polynomial Regression (Polynomial Regr), and Isotonic Regression (Isotonic Regr) in modeling daily reference evapotranspiration (ETo) at IARI, New Delhi. The models were developed considering full and limited dataset scenarios. The efficacy of the constructed models was assessed against the Penman-Monteith (PM56) model estimated daily ETo. Results revealed the under full and limited dataset conditions, XGBoost Ensemble Tree gave the best results for daily ETo modeling during the model training period, while in the testing period under scenarios S1(Tmax) and S2 (Tmax, and Tmin), the Isotonic Regr models yielded superior results over other models. In addition, the XGBoost Ensemble Tree models outperformed others for the rest of the input data scenarios. The XGBoost Ensemble Tree algorithms reported the best values of correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Thus, we recommend applying the XGBoost Ensemble Tree algorithm for precisely modeling daily ETo in semi-arid climatic conditions.

Plasmalogens: Free Radical Reactivity and Identification of Trans Isomers Relevant to Biological Membranes.

Plasmalogens are membrane phospholipids with two fatty acid hydrocarbon chains linked to L-glycerol, one containing a characteristic cis-vinyl ether function and the other one being a polyunsaturated fatty acid (PUFA) residue linked through an acyl function. All double bonds in these structures display the cis geometrical configuration due to desaturase enzymatic activity and they are known to be involved in the peroxidation process, whereas the reactivity through cis-trans double bond isomerization has not yet been identified. Using 1-(1Z-octadecenyl)-2-arachidonoyl-sn-glycero-3-phosphocholine (C18 plasm-20:4 PC) as a representative molecule, we showed that the cis-trans isomerization can occur at both plasmalogen unsaturated moieties, and the product has characteristic analytical signatures useful for omics applications. Using plasmalogen-containing liposomes and red blood cell (RBC) ghosts under biomimetic Fenton-like conditions, in the presence or absence of thiols, peroxidation, and isomerization processes were found to occur with different reaction outcomes due to the particular liposome compositions. These results allow gaining a full scenario of plasmalogen reactivity under free radical conditions. Moreover, clarification of the plasmalogen reactivity under acidic and alkaline conditions was carried out, identifying the best protocol for RBC membrane fatty acid analysis due to their plasmalogen content of 15-20%. These results are important for lipidomic applications and for achieving a full scenario of radical stress in living organisms.

Divertor power spreading in the Divertor Tokamak Test facility for a full power scenario with Ar and Ne seeding

This work describes integrated numerical modelling applied to Divertor Tokamak Test (DTT) scenarios with tungsten wall and divertor in single null configuration using the COREDIV code, which self-consistently solves 1D radial transport equations of plasma and impurities in the core region and 2D multi-fluid transport in the scrape-off layer (SOL). COREDIV code simulations have been performed and compared to the already published solutions from JETTO for DTT full power discharges with Ar seeding. The influence of the particle transport on the fuelling and flux to the plate is analysed. The main conclusion from the performed simulations is that Ne and Ar radiate effectively in the SOL and no difference was found in the fuelling properties between them. The fuelling increases with increase in radial transport in the core region. It has been found that the rise in the diffusion in the core plasma has a small influence on several global plasma parameters, such as plasma radiation, impurity concentration and fluxes to the divertor plate, but has a strong effect on the fuelling. The fuelling and deuterium flux to the plate decrease almost linearly with decreasing plasma density, keeping = constant.

Finite element simulation of growth modulation during brace treatment of adolescent idiopathic scoliosis.

Adolescent idiopathic scoliosis (AIS) is a spine deformity whose progression during growth is affected by asymmetrical loads acting on the spine. The conservative brace treatment aims to limit the deformity's progression until the end of skeletal growth. This study's objective was to develop a patient-specific finite element model (FEM) simulating immediate in-brace correction and subsequent growth modulation over 2 years of treatment. Thirty-five retrospective AIS cases with documented correction over 2 years were analyzed. For each case, a patient-specific FEM was built, and in-brace correction was simulated. Vertebral growth and its modulation were modeled using simulated pressures on epiphyseal vertebral growth plates, including a compliance factor representing the recorded brace wear. The simulated Cobb angles, thoracic kyphosis, lumbar lordosis and apical vertebral rotation were compared with the actual measurements immediately in-brace and out-of-brace at the 2-year follow-up. Treatment outcomes according to simulated compliance scenarios of no brace-wear vs. full brace-wear were also computed. The average immediate in-brace difference between the simulated and actual Cobb angle was 4.9° (MT) and 3.7° (TL/L). 2-year out-of-brace, it was 5.6° (MT) and 5.4° (TL/L). The no brace-wear and full brace-wear compliance scenarios resulted respectively in 15/35 (43%) and 31/35 (89%) simulated spine deformities progressing by less than 5° over 2 years of treatment. Clinical significance: the FEM's ability to simulate the final correction with an accuracy on the order of the radiological measurements' interoperator reproducibility, combined with its sensitivity to brace-wear compliance, provides confidence in the model's predictions for a comparative context of use like improving a brace's design before its application. This article is protected by copyright. All rights reserved.

Greenhouse gas emissions, land use and employment in a future global bioplastics economy

Plastics are among the most versatile materials in the world but pose a threat to the environment due to pollution of land, water, and air. Research has focused on developing natural substitutes for fossil-derived, non-degradable plastics, principally by replacing oil-based feedstocks with renewable feedstocks and by producing biodegradable alternatives, preferably from such feedstocks. With a resultant significant growth in production capacity for biobased plastics, there is a need to better understand the global implications of such a transition. Here we conduct a global multi-regional input-output analysis of plastic supply chains, with specific consideration of technological requirements of conventional and biobased plastic resources, monomers, polymers and final plastic products. We analyse a set of scenarios with varying levels of replacement of conventional processes with biobased processes. Under a full replacement scenario, we find greenhouse gas emission savings of 369 Mt (–1% of global emissions), an increase in land use of 65 Mha (+1.2% of global land use), and the generation of an additional 18 million jobs (+0.6% of global employment). Whilst principally these results show positive emission and employment outcomes, policy will be needed to further guide the transition in order to increase uptake, and to guard against potential negative outcomes related to increased land requirements.

Comprehensive Mechanistic Scenario for the Cu-Mediated Asymmetric Propargylic Sulfonylation Forging Tertiary Carbon Stereocenters

Metal-catalyzed propargylic transformations represent a powerful tool in organic synthesis to achieve new carbon-carbon and carbon-heteroatom bonds. However, detailed knowledge about the mechanistic intricacies related to the asymmetric formation of propargylic products featuring challenging heteroatom-substituted tertiary stereocenters is scarce and therefore provides an inspiring challenge. Here, we present a meticulous mechanistic analysis of a propargylic sulfonylation reaction promoted by a chiral Cu catalyst through a combination of experimental techniques and computational studies. Surprisingly, the enantio-discriminating step is not the coupling between the nucleophile and the propargylic precursor but rather the following proto-demetalation step, a scenario further validated by computing enantio-induction levels under other previously reported experimental conditions. A full mechanistic scenario for this propargylic substitution reaction is provided, including a catalyst pre-activation stage, a productive catalytic cycle, and an unanticipated non-linear effect at the Cu(I) oxidation level.

The importance of flexible hydropower in providing electricity stability during China’s coal phase-out

A rapid coal phase-out is critical if China is to achieve carbon neutrality by 2060. However, this presents a significant challenge in maintaining system flexibility as the number of coal power stations reduce and renewable energy penetration increases. Here we analyze the interactions between flexibly operating hydroelectric facilities in balancing the decommissioning of a typical 600 MW coal-fired power unit across three different provinces with different electricity mixes. Rather than using a constant parameter for the carbon emission intensity for coal, as used in previous studies, we use dynamic intensities across different output levels. Accounting for the higher emission intensities of coal power plants running at below optimal efficiency to provide flexibility increases emissions from 3.4 % to 11.1 % depending on the region. The coal phase-out lowers flexibility indicators, with loss-of-load increasing in the full phase-out scenario in the absence of mitigating technologies such as electricity storage. Flexible hydropower eases peak demand issues significantly and the operating range of hydro-units has no significant effect on system flexibility or emissions. Overall, hydropower shows large potential in supporting decarbonization while maintaining system flexibility, especially for coal-dominated countries with abundant hydropower resources.

GNSS Antenna Pattern Prediction and Placement Optimization: A Prototype Method Using Machine Learning to Aid Complex Electromagnetic Simulations Validated on a Vehicle Model

An antenna’s radiation pattern is dependent on its geometrical characteristics and its antenna’s surroundings, materials, and geometries. As such, to predict the antenna’s performance in complex environments, such as that of small antennas on large vehicles, it is necessary to obtain a model that represents such a full scenario, so that the simulation may be accomplished in the process of antenna design and placement. Due to the complex and electrically large nature of some electromagnetic problems, the detailed representation, even for a simplified model, may imply a large computational effort, both in terms of time and memory, is needed to perform the simulation. This paper evaluates how machine learning models can be used to mitigate the computational effort required to predict the behavior of antennas requiring complex modeling. It is proposed to start from a more simplified model of the electromagnetic structure to obtain a prediction for the correct solution, without needing to simulate the full structure in every iteration, and to combine this with prediction algorithms to obtain the solution of the full problem. The proposed solution uses convolutional neural networks (U-Net) of a certain accuracy to help with the correct placement of small antennas on autonomous vehicles. The standard approach requires the simulation of a full model at each test position, requiring high computational time and memory. With this new proposal, it is possible to analyze more positions and radiation patterns in a much shorter time, and with less memory, when compared with the solution from the full model. Along with this methodology for each simulation, a Bayesian optimizer is proposed to improve the search process for the best location, leading to a reduction in the required steps. This methodology was applied to support the correct positioning of a GNSS antenna with reference to a set of performance indicators required for autonomous vehicles, but it can be also applied to larger and more complex structures, allowing one to reduce the simulation time of a large electromagnetic structure and the search time for the optimum location.

Multiphysics Modeling of Impurity Transport for FNSF Startup Scenario with ERO2.0

This study focuses on performing a multiphysics study using the ERO2.0 and UEDGE codes for two standard double null configurations for the Fusion Nuclear Science Facility: (a) 100% recycling and (b) 99% recycling. Results show that the main contributor to tungsten erosion along the divertor plates is impurities from the midplane waveguides. In addition, the standard high-recycling case (100% recycling) shows a significantly higher buildup of impurities along the divertor tiles during the startup phase, which can lead to a higher increase of energy loss in the plasma during steady-state operation. Last, for high recycling, anomalous diffusion can dominate over parallel field diffusion. The work performed in this study can be iteratively applied to a full operation scenario with additional physics such as those from neutrals, wall shaping, and additional external fields.

The Impact of Froth Launders Design in an Industrial Flotation Bank Using Novel Metallurgical and Hydrodynamic Models

In flotation cells, especially in large flotation units, froth management is a crucial variable that should be considered during the design phase or optimized to improve the performance of existing flotation circuits. This paper presents a simulation evaluation of the effect of launder design on the metallurgical performance of an industrial flotation circuit consisting of five TankCell® e630 (630 m3) cells in a Cu rougher duty. This analysis was carried out using a new industrial simulator that includes novel metallurgical and hydrodynamic models, developed from a wide database collected from many industrial concentrators. This tool is currently incorporated into HSC Chemistry® software and allows evaluating the effect of launder design on mineral froth recovery, water recovery, entrainment, and other variables. The industrial flotation circuit was evaluated under different launder design scenarios, considering an actual flotation circuit that includes TankCell® e630 cells for calibration and as a reference (baseline). Firstly, two different designs were evaluated in the full circuit: a standard launder design and a new launder technology. It was found that the new launder technology enabled improvement of the mineral recovery along the circuit, mainly for coarse particles, due to the improvement in froth mineral recovery. Next, a partial upgrade of the launder design along the circuit was analysed. Thus, the new launder technology was evaluated in the first and the last two cells of the bank. The results showed that upgrading the launders in different cells along the circuit delivered an increase in the final recovery with respect to the baseline, with a partial impact on the concentrate grade. However, these changes are less than those when evaluating the full upgrade scenario. A partial launder upgrade either in the first or last two cells of the bank showed similar final recoveries, but the latter enabled a slightly higher concentrate grade (about 1% higher) to be achieved. Finally, the evaluation of launder design using the industrial simulator made it possible to estimate its effect on variables that are not commonly obtained from plant surveys, including superficial gas rates at the froth surface level, froth recovery per particle size, collection recovery per particle size and liberation, gangue entrainment, and bubble loading grade.

Study of Energy Transition Paths and the Impact of Carbon Emissions under the Dual Carbon Target

In recent years, the world’s environmental problems have become increasingly serious, and energy transition and carbon emission issues have gradually gained attention from various countries. China has promulgated several policies and adopted various reform measures to achieve a comprehensive energy transition and carbon neutrality as soon as possible. Therefore, this study makes researches and forecasts the energy transition and carbon emissions in China under the dual carbon target. A LEAP (Long range Energy Alternatives Planning) model is developed to analyze the energy parameters of Beijing under various scenarios and to provide a quantitative analysis basis for the energy transition path. The obtained experimental results indicate that the energy demand under the partial energy transition scenario and full energy transition scenarios are 68.651 million tons of standard coal and 75.759 million tons of standard coal, respectively, forming an effective control, while the carbon emissions both achieve the carbon peak in 2025 at 81.903 million tons and 80.624 million tons respectively, and achieve 46.588 million tons in 2060. The carbon-neutral pathway reaches the carbon peak in 2025, and approaches full energy transition in 2035, and finally reaches the full energy transition control effect in 2060. To date, most of the relevant studies have been conducted in a qualitative way, and the amount of quantitative analysis is insufficient. At the same time, research on the development path specifically at the city level is relatively insufficient as well. Therefore, the present study can provide a theoretical basis for specifying the promotion method of urban energy transformation and the path of carbon emission reduction.

Residual correlation and ensemble modelling to improve crop and grassland models

Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-nitrogen fluxes, productivity and sustainability. There is mounting evidence that with some site-specific observations available for model calibration (with vegetation data as a minimum requirement), median outputs assimilated from biogeochemical models (multi-model medians) provide more accurate simulations than individual models. Here, we evaluate potential deficiencies in how model ensembles represent (in relation to climatic factors) the processes underlying biogeochemical outputs in complex agricultural systems such as grassland and crop rotations including fallow periods. We do that by exploring the correlation of model residuals. We restricted the distinction between partial and full calibration to the two most relevant calibration stages, i.e. with plant data only (partial) and with a combination of plant, soil physical and biogeochemical data (full). It introduces and evaluates the trade-off between (1) what is practical to apply for model users and beneficiaries, and (2) what constitutes best modelling practice. The lower correlations obtained overall with fully calibrated models highlight the centrality of the full calibration scenario for identifying areas of model structures that require further development.

A framework for a structured problem-solving approach with experimental design as the focus for industrial processing environment

This article focuses on developing a framework for industrial environments to provide a structured problem-solving approach based on experimentation as a basis to assist analysts and management for strategic decision making for process improvement. The process for developing this generic framework was through an analytical process improvement process case study for a company in an industrial environment. The research methodology includes an interpretivist approach followed by a positivist approach and ends with a constructivism worldview due to the Experimental design approach design depicted by this framework. The summarized goals were, expanding Design of Experiments (DOE) as a statistical approach to complement existing methods and methodologies used for Data Mining, validate the integrity of data through a refining process and applying DOE in combination with traditional Data Mining techniques. The importance for developing this framework was to experiment with historical data, based on real process data then to predict future process behaviour, using full and fractional DOE design scenarios which allows the analyst not to have a one-dimensional analytical approach but to evaluate which design fits the data best. No risk of costly process failures due to experimenting into the unknown by utilizing historic data by applying experimentation when evaluating different processing scenarios for possible product improvement and to provide an alternative statistical approach for Data Mining in an industrial environment for screening independent and dependent variables for a DOE model.