Multistage System Research Articles

Numerical simulation of a multistage magnetic refrigeration system in the temperature range of liquid hydrogen

Magnetic refrigeration is an important new refrigeration technology in the temperature range of liquid hydrogen. A multistage magnetic refrigeration system between 20 K and 80 K has been proposed, which includes three-stage active magnetic regenerators (AMRs). Based on the multistage system, a two-dimensional transient AMR model is established to research the cycle performance mechanism by considering the compressibility of helium, which has not been done before. Firstly, to simplify the simulation difficulty, the AMR model with ideal magnetocaloric materials (the constant isothermal entropy change of 5.4 J/(kg·K) at 1 T) are investigated in the temperature range between 60 K and 80 K. Some parameters including particle diameter, fluid flow fraction, and filling mass of magnetocaloric materials have been compared and analyzed. Based on the above simulation results, the AMR model with real magnetocaloric materials has been done. In the three-layer AMR, the maximum cooling power of 71.04 W at the cooling temperature of 60 K has been obtained, which is 80.17% larger than that of the one-layer AMR. Then, the cooling performance of the multistage magnetic refrigeration system has been studied. In the system, six magnetocaloric materials are filled in three-stage AMRs based on the filling method of adiabatic temperature change. With the optimal mass flow rates of each stage AMR, the maximum cooling power of 40.09 W has been obtained in the multistage system, and the corresponding thermodynamic second efficiency has been 28.14%.

Geothermal-solar integrated Multistage Flash Distillation Cogeneration system: A cleaner and sustainable solution

The geothermal water is at shallow depth at most of the places in Gujarat. The objective of this paper is to develop a green desalination system which can provide potable fresh drinking water. In this study, a Multi-Stage Flash Distillation system (MSF system) is designed for desalination of geothermal water where the energy source for the system is solar. Based on the temperature requirement of the MSF system solar Evacuated Tubes (ETC) are used for increasing the water temperature in the system. Python programming and Coolprop properties was used for the design and thermodynamic analysis of the whole system of both ETC and MSF desalination system. Simulations were performed to produce heat from ETC with the geothermal MSF system for generation of steam and salt. The details of material selection and corrosion protection techniques are also reviewed in the paper. The results of the simulation were satisfactory, and the salt concentration of the water was reduced up to the standards of freshwater i.e., it was reduced from up to 194 PPM. Cost estimation of the system was also performed in the form of CAPEX and OPEX expenditures. The price of water per cubic meter was estimated to be 0.1034 USD.

Rigid-flexible coupled modeling of compound multistage gear system considering flexibility of shaft and gear elastic deformation

Compound multistage gearing systems are widely used in rotating machinery to provide high speed-reduction ratios and heavy load capacity. Modeling and characterizing the dynamic behavior of compound multistage gear transmission systems provides valuable insights for the design and fault diagnosis of transmission systems. However, modeling compound multistage gear transmission systems still has some challenges due to 1) the complex interactions among multistage gear systems and 2) the flexibility, e.g., gear elastic deformation related to gear time-varying mesh stiffness (TVMS), which is affected by load and has not yet been addressed in analytical method, and shaft flexibility. In this paper, a multistage rigid-flexible model of compound multistage gear transmission systems is proposed to comprehensively understand their dynamic behaviors. The proposed method concerns the flexibility of gears and shafts and further constructs a dynamic analysis model. By this, the load-dependent TVMS considering gear tooth elastic deformation is calculated and compared with the existing method. After that, the ordinary and partial differential equations of gears and shafts are fused to characterize the coupling behaviors among multistage subsystems. Finally, the proposed model is solved using the variables separation method and modal superposition method. The accuracy of the constructed model is verified by experimental response collected from a wind turbine drive train test rig. All the characteristic frequencies and the coupled dynamic response of the whole system are obtained and analyzed. The modal energy distribution of the proposed model is depicted to illustrate the flexibility and load influence on the system operational modal, which provides a theoretical foundation for the design and fault diagnosis of a compound multistage gear system.

Implementation of extreme ultraviolet spectroscopy on a sheared-flow-stabilized Z pinch

A diagnostic for extreme ultraviolet spectroscopy was fielded on the sheared-flow-stabilized (SFS) fusion Z-pinch experiment (FuZE-Q) for the first time. The spectrometer collected time-gated plasma emission spectra in the 5–40 nm wavelength (30–250 eV) range for impurity identification, radiative power studies, and for plasma temperature and density measurements. The unique implementation of the diagnostic included fast (10 ns risetime) pulsed high voltage electronics and a multi-stage differential pumping system that allowed the vacuum-coupled spectrometer to collect three independently timed spectra per FuZE-Q shot while also protecting sensitive internal components. Analysis of line emission identifies oxygen (N-, C-, B-, Be-, Li-, and He-like O), peaking in intensity shortly after maximum current (>500 kA). This work provides a foundation for future high energy spectroscopy experiments on SFS Z-pinch devices.

Rancang Bangun Dan Kinerja Alat Borazsilab Filter Dengan Sistem Filtrasi

Research has been carried out on the BorazSiLab Filter design and performance used filtration system. The selected filtration mechanism is a multi-stage filtration system. The filter material used is lontar shell activated charcoal, coconut fiber, sand, and zeolite. The purpose of this study is to test the performance of the tool (effectiveness and efficiency) filled with the four materials above. The implementation stages of this research are divided into two, namely fabricating the BorazSiLab Filter tool and testing the reliability of tool. To facilitate tool fabrication, it is necessary to design tools in the form of working drawings of 2D shapes and 3D geometry so that they are easy to understand. The reliability test was carried out on the dimensions of the filter box, the surface area and volume of the material, the filtration discharge, and the water quality (pH, temperature, odor, and colors). The results showed that the tool was made of a glass box measuring 20×20×20 cm. The filtration time is fixed at 45 minutes. The measured filtration efficiency and effectiveness of the filter material surface area, filter media volume, filtration discharge before (Qin) and after (Qout) filtration are 0,0022 m2 (one tea bag) to 0,0044 m2 (two tea bag); 3,3×105 m3 (one tea bag) to 5,81×105 m3 (two tea bag); 1,1423 L/s; and 0,00046 L/s. Preliminary tests for water quality before and after for the parameters of odor, pH, and temperature values, respectively, are odor to odorless; colored to colorless pH = 7; temperature = 26,1 ± 3 °C to 27,9 ± 3 °C. Therefore, the BorazSiLab Filter can be used as a filtration tool for wastewater treatment.

Optimization of a multistage liquid desiccant dehumidifier by neural networks & particle swarm techniques

To overcome the inconsistent and limited dehumidification by single stage dehumidification system, a novel multistage, reciprocating dynamic liquid desiccant dehumidification system has been proposed to dehumidify larger space consistently. Experimental investigation on the proposed unit has been conducted by varying air velocity, cam shaft speed, and inlet relative humidity (RH). System's performance in terms of moisture removal rate (MRR), dehumidification efficiency (DE) and energy consumed has been compared with that of a single stage system. It is observed that multistage system performed better than the single stage system in terms higher MRR and efficiency. To optimise the system performance, neural network-based Particle Swarm optimization (PSO) has been employed. Inertia weight distribution for PSO has been obtained by the statistical Taguchi method. PSO optimized results for randomly distributed weights have been compared with the Taguchi given weights. It is found that the performance of the PSO with this method is better than the former in terms of optimized paramters. PSO predicted optimum input variables are found as air velocity of 8.2 m/s, inlet RH of 90% and cam speed of 15.16 rpm. The maximum performance parameters obtained are MRR of 7.549 g/s, Dehumidification efficiency of 0.762 and energy consumption of 2165.4 Watts. Confirmation experiments indicated that PSO predicted results are in well agreement with the experimental results with a maximum deviation of 1.8%. Current work predicts and optimizes the dehumidification performance which minimizes experimentation time and also the cost involved.

Overall evaluation of a multi-stage reverse electrodialysis reactor series system for organic wastewater treatment and power generation

A multi-stage reverse electrodialysis reactor (REDR) series system for organic wastewater treatment and power generation is proposed in this paper. An energy evaluation method is developed for the first time by analyzing the electrical energy and oxidative degradation energy conversion efficiencies of the system. The influences of the current and feed solution velocity on the overall performances of the system are investigated by experiments. Results demonstrate that a high current can enhance the gross output power but reduce the net output power and total energy conversion efficiency. A peak value of the total COD removal can be obtained at an appropriate current. A low feed solution velocity can improve the performance of the system except for the lower net output power. Although the electrical energy conversion efficiency reduces as the serial number of REDR increases, the oxidative degradation energy conversion efficiency increases significantly, which results in the total energy conversion efficiency enhancement. Under the given experimental conditions, the maximal values of the net output power, total COD removal and total energy conversion efficiency are 1.05 W, 450.98 mg·L−1, and 11.41%, respectively.

Techno-economic analysis of conversing the low-grade heat to hydrogen by using reverse electrodialysis – Air gap diffusion distillation coupled method for iron and steel industry

This study devotes to the reverse electrodialysis (RED) – air gap diffusion distillation (AGDD) coupled system of converting the low-grade waste heat recovered from the steel production to hydrogen for steelmaking. This approach not only recovers waste heat but also reduces carbon emissions. To assess the feasibility of the coupled system, a techno-economic model has been developed. The influences on the levelized cost of hydrogen (LCOH) of the ion exchange membranes (cost and lifetime), the operating condition of RED stack (current density and feed solution velocity), the AGDD unit (feed solution flowrate), and the numbers of RED stacks in a multistage series system are investigated. The high price of ion exchange membrane is currently the main factor causing non-commercialization, which is almost 6.63–19.9 times larger than the LCOH of hydrogen production from renewable electricity. The membrane lifetime is the critical factor in increasing the maximum permitted membrane cost. Assuming the membranes cost is 1 RMB/m2, the optimal current density, feed solution velocity (in RED stack), and the solution volume flowrate (in AGDD unit) of are 50 A/m2, 0.00075 m/s, and 0.7 m3/h, respectively. The series systems take a considerable advantage in reducing the required AGDD units compared with one stage.

ANALYSIS OF THE DYNAMICS OF THE INTEGRAL RATING OF RUSSIAN COMPANIES ACCORDING TO A TWO-STAGE PROCEDURE, TAKING INTO ACCOUNT THE RISK

The article is devoted to the development and justification of a multi–stage system of ranking and indexing of leading companies of the most important energy production sectors of the Russian economy - electric power industry and the oil and gas industry. The authors use a financial analytics apparatus based on regular financial (accounting) statements of companies according to accepted forms, a balance sheet and a report on financial results are applied. The authors developed and tested a methodology for sub–indexing important indicators of financial statements: total assets, capital structure - borrowed and own funds, net profit. The author's methodology allows for an express analysis of important indicators of companies' performance and to build an integral rating. The authors performed a comparative analysis of the rating of the leading companies in the oil and gas and electric power industry of Russia for several periods and obtained results that will allow making decisions that contribute to the stable development of companies for many years.

Treatment of Landfill Leachate Reverse Osmosis Concentrates by Advanced Oxidation-Heterotrophic Nitrification-Aerobic Denitrification Combination process.

This study aimed to develop a multistage treatment system for highly toxic wastewater named reverse osmosis concentrates of landfill leachate. Therefore, a combination of the ammonia stripping process (ASP), catalytic ozone oxidation process (COP), and heterotrophic nitrification-aerobic denitrification process (HNADP) was proposed and the quality of effluent was evaluated for the concentration of chemical oxygen demand (COD), ammonia nitrogen (NH4+-N), and total nitrogen (TN). ASP had moderate removal efficiency of NH4+-N, and TN in the effluent. COP was catalyzed by cerium-supported-activated carbon achieved good performance in disposal of COD. The effluent of HNADP had the most significant removal efficiency of COD, NH4+-N, and TN. As a result, the effluent of combined process successfully met the discharge standards for NH4+-N and TN according to Table 1 of GB 16889-2008 in China. To investigate the microbial mechanism of pollutant removal in HNADP, 16S rRNA high-throughput sequencing was performed and the results suggested that the relative abundance and diversity of microorganisms fluctuated with the changes of COD/TN ratio in HNADP. Truepera and Halomonas were identified as the key genera involved in the simultaneous degradation of COD and nitrogen-containing pollutants, the functional genes (hao, amoA, nirS, and nirK) were predicted in nitrification and denitrification process. Overall, this study demonstrates a feasible multistage system for treatment of concentrates and propose that further explorations of combined techniques may lead to even more satisfactory removal efficiencies.

Production quality prediction of multistage manufacturing systems using multi-task joint deep learning

A multistage manufacturing system with multiple manufacturing stages is the key and main production mode for enterprises to achieve lean production. Due to the variation propagation between stages and multiple related quality prediction tasks, it is difficult to accurately predict the quality of multistage manufacturing systems with multiple tasks. Traditional single stage and single task quality prediction methods permit the multi stages and multiple tasks separately, which ignore multi-stage effects or the quality-related relationship between multiple quality output indicators and reduce the efficiency of quality prediction. In this paper, a production quality prediction framework based on multi-task joint deep learning is proposed to simultaneously evaluate the multi-task quality of all stages in a multistage manufacturing system. To be specific, variation propagation cumulative impact between multiple manufacturing stages is innovatively expressed by designing a multi-scale convolutional neural network with control gates (MCNN-CG) to extract and propagate data features. Production quality with multi-tasks at all stages is jointly predicted by designing a multi-layer multi-gate mixture-of-experts multi-task (ML-MMoE) model with reducing multi-task predictive loss simultaneously. The soft parameter-sharing strategy and multi-gate attention strategy are separately designed to ensure information sharing while learning personalized features of tasks to improve quality prediction accuracy of each task. In addition, a loss function based on homoscedastic uncertainty and regularization is designed to automatically learn the weight between multi-stage and multi-task losses. Experiments on multistage assembly test data of an inertial navigation manufacturing system show that the proposed method performs better than traditional models. Compared to the single-stage model, the proposed multistage model has an average improvement of 8.5%, 20.0% and 23.3% in R2, MAE and RMSE respectively in the second stage. Compared with the traditional multi-stage model, the proposed model has an average improvement of 1.7%, 6.2% and 9.8% in R2, MAE and RMSE respectively in the second stage.

A click chemistry-based, free radical-initiated delivery system for the capture and release of payloads

Click chemistries are efficient and selective reactions that have been leveraged for multi-stage drug delivery. A multi-stage system allows independent delivery of targeting molecules and drug payloads, but targeting first-phase materials specifically to disease sites remains a challenge. Stimuli-responsive systems are an emerging strategy where common pathophysiological triggers are used to target payloads. Oxidative stress is widely implicated in disease, and we have previously demonstrated that reactive oxygen species (ROS) can crosslink and immobilize polyethylene glycol diacrylate (PEGDA) in tissue mimics. To build on these promising results, we present a two-step, catch-and-release system using azide-DBCO click chemistry and demonstrate the capture and eventual release of a fluorescent payload at defined times after the formation of a PEGDA capturing net. The azide component is included with radical-sensitive PEGDA, and the payload is conjugated to the DBCO group. In cell-free and cell-based tissue mimic models, azides were incorporated at 0–30% in the first-phase polymer net, and DBCO was delivered at 2.5–10 µM in the second phase to control payload delivery. The payload could be captured at multiple timepoints after initial net formation, yielding a flexible and versatile targeting system. Matrix metalloproteinase (MMP)-degradable peptides were incorporated into the polymer backbone to engineer fluorescent payload release by MMPs, which are broadly upregulated in diseases, through degradation of the capture net and directly from the DBCO. Taken together, this research demonstrates proof-of-principle for a responsive and clickable biomaterial to serve as a multi-potent agent for the treatment of diseases compounded by high free radicals.

Multi-stage stochastic planning for a long-term low-carbon transition of island power system considering carbon price uncertainty and offshore wind power

The characteristics of isolated grids lead island power systems to rely on stable conventional generation, which is not environmentally friendly and has high carbon emissions. This paper proposes a low-carbon transition model for island power systems considering large-scale offshore wind resources and the long-term uncertainty of carbon prices. First, a detailed model for offshore wind power systems is proposed considering the interaction of multiple wind farms and three transmission technologies. Second, a multi-stage power system planning model is established including the upgrading and decommissioning of thermal units. Meanwhile, an improved long-term carbon price uncertainty modeling method based on the binomial tree is proposed. Then, focusing on the uncertainty of carbon prices, a multi-stage stochastic optimization model is established, and the stochastic nested decomposition algorithm is used to solve the problem. Finally, numerical results show that the low-carbon transition develops plenty of offshore wind power as the primary power source and usually adopts a fractional frequency transmission system for offshore wind integration. The uncertainty of carbon prices has a significant impact on the transition: in the high carbon price realization, the annual carbon emissions are only 0.088 Mt, while in the low carbon price realization, the annual emissions are 0.255 Mt.

Decoding the performance of module-scale interfacial desalination from a thermodynamic perspective

Passive solar-driven interfacial evaporation (PSDIE) is a highly efficient desalination technology that localizes heating at the evaporation surface, resulting in low heat loss and high evaporation rates. While many studies have focused on improving solar absorber performance, few have systematically analyzed the PSDIE thermodynamics. In this study, we propose a novel PSDIE distiller that effectively avoids salt accumulation. A module-scale model is developed that captures the mass and heat transfer characteristics of the distiller, and comprehensive thermodynamic analysis is performed. Our results show that the relative thickness between the evaporation and condensation layers, namely α, is a critical design parameter that optimizes process performance. An evaporation restricted region (α>5) and a condensation restricted region (α<5) can be identified. We also investigate the influence of hydrophobic membrane properties, finding that their effects depend on α. Our analysis demonstrates that inlet salinity of seawater feed has a detrimental effect on the water productivity of the distiller, while distiller with larger α tends to be more salt tolerable. Finally, we propose a design for a multistage distiller system and evaluate its water production performance, the results show that additional evaporation stages are beneficial to the distiller with smaller α.

Digitalization platform for data-driven quality management in multi-stage manufacturing systems

Digital transformation is driving the current technological trends in manufacturing. An integral constituent is a communication between machines, between machines and humans, or between machines and products. This extensive communication involves large volumes of data. Many manufacturers apply data analytics (e.g., for quality management or improvement purposes) to translate the data into a business value. However, isolated, rigid, and area-specific IT solutions often carry this out. Today’s complex manufacturing requires quality management approaches that constitute a holistic view of and understanding of process–product interactions along the process chain instead of focusing solely on single processes. A novel platform approach to support quality management in manufacturing systems is proposed in this paper to overcome this deficit. It integrates state-of-the-art concepts of IT with modeling approaches for planning and operation of quality management. A conceptual framework and the technical architecture for implementing a digitalization platform are presented in this regard. Moreover, the approach is validated and implemented within a web application based on a use case of data-driven quality management in electronics production.

Experimental validation of multi-stage optimal energy management for a smart microgrid system under forecasting uncertainties

This paper proposes a Multi-stage Energy Management System (MS-EMS) for power distribution in a smart microgrid comprising a photovoltaic system (PV), an Energy Storage System (ESS), and connected to an Electrical Power Grid (EPG). The proposed MS-EMS consists of two layers, the Anticipative Layer (AL) and the Reactive Layer (RL). The AL uses a Multi-objective particle swarm optimization (MOPSO) algorithm to determine the optimal power setpoints for each source over the next 24 h based on predicted energy demand and solar power generation. The RL utilizes real-time data collection and Extremum-Seeking optimization (ES) to compensate for prediction uncertainties by adjusting the ideal power setpoints in real-time. In addition, this paper addresses both the issues of management and prediction. It introduces a forecasting system that effectively predicts the next 24 h of load demand and PV power generation using actual data, by integrating a physical model with artificial neural networks (ANN). The efficiency of the proposed MS-EMS is highlighted using experimental data collected in real-time on a physical bench in Morocco. The performances of the proposed MS-EMS system are compared with those of existing EMS strategies, including an existing industrial solution. The comparison shows that the proposed MS-EMS substantially overperforms in terms of three criteria: energy bill, battery degradation cost, and peak-to-Average Ratio (PAR). Despite forecasting uncertainties, the experimental test shows a remarkable 6% gain in the reduction of the energy bill and PAR compared to an offline strategy (where uncertainties were not considered). Furthermore, it achieved a 5% and a 3% gain in the reduction of the energy bill and PAR, respectively compared to an online strategy (focused on real-time optimization). Additionally, compared to an existing industrial solution, the MS-EMS achieves a reduction in energy bill and PAR of 9% and 56% respectively while taking into account battery degradation constraints described in this study.

Model-based optimization of multistage ultrafiltration/diafiltration for recovery of canola protein

The ultrafiltration/diafiltration (UF/DF) process is a crucial step in the canola protein isolation process from rapeseed meal. The process involves using a multi-stage membrane system to separate components of the mixture. As diafiltration dilutes the feed stream in the ultrafiltration system, a large amount of diafiltration water is required. Reducing the diafiltrate for sustainability reasons can be done by carefully selecting process variables or using recycle streams. However, finding the optimum process variables can be a meticulous process if performed experimentally or via trial and error. In this study, we performed an optimization using a mechanistic model integrated with a genetic algorithm to aid in finding an optimum combination of process variables. The mechanistic ultrafiltration model was derived by taking into account transport phenomena within the filtration system. Parameters were characterized experimentally in term of viscosity coefficient, membrane resistance, cake porosity, aggregate diameter equivalent, and material compressibility factors. Using the mechanistic model-based optimization in combination with actual experimental values, the performance of a four-stage UF/DF system could already be improved despite fixing the configuration, albeit at the cost of a reduction in purity. Further improvement could be achieved by using recycle streams. The optimized system achieved a diafiltrate reduction of up to 79%, an increase of purity of up to 31%, and an increase of dry matter content of up to 18%, while maintaining the product purity of the reference set-up.

Experimental study and theoretical analysis on lateral stiffness of RC frame-frame truss composite wall

Two reinforced concrete (RC) frame-frame truss composite wall (FTCW) specimens with a scale of 1:2 are implemented for cyclic loading test. The test results of phenomenon, hysteretic curves, backbone curves and stiffness degradation curves are obtained, and the seismic performance of shear capacity, ductility, lateral stiffness are analyzed. The lateral stiffness of RC frame-FTCW and the horizontal displacement are mechanical analyzed, and the results are compared with the test results. The test behaviour shows that a multistage energy consuming system that FTCW works before RC frame and the internal diagonal struts work before the outer frame inside the FTCW, forming a multistage energy consumption system for the design purpose of earthquake resistant structures. The point that the embedded angle steel effectively extend the elastic stage of the internal diagonal struts and the point that the amount of FTCW is proportionateto the shear capacity and lateral stiffness are also concluded by the test results. The comparison results of lateral stiffness between mechanical analysis and test results show that the reduction coefficients in different stages and the mechanical analysis are practical and significant.

Research on Pump Speed Control System Based on Fuzzy PID

In this paper, based on MATLAB/simulink, the PID vector frequency conversion control of permanent magnet synchronous motor has better drive control performance in the multi-stage centrifugal pump speed control system. The electric-hydraulic coupling system of centrifugal pump has a good evaluation and research function. In view of the low efficiency of the traditional PID control method and the poor adaptability to the change of working environment, a vector control method of permanent magnet synchronous motor based on adaptive and self-adjusting fuzzy control principle is proposed for the speed regulation system of water pump. The simulation results show that the PMSM system controlled by the fuzzy PID controller is superior to the traditional PID control, and has better disturbance resistance, dynamic and steady performance.

Нечеткологическая модель многостадийной химико-энерготехнологической системы переработки мелкодисперсного рудного сырья

The results of the study, the purpose of which was to build a software model of a multi-stage integrated system for processing finely dispersed ore raw materials, are presented. The role of such raw materials can be processed waste at mining and processing plants of apatite-nepheline and other types of ores, which accumulate in large volumes in tailing dumps. They create a significant environmental threat in the territories adjacent to the plants due to weathering, dust formation, penetration into the soil and aquifers of chemical compounds and substances hazardous to human health. Therefore, the improvement of existing production processes, the development of new technological systems for mining and processing plants, including the application of the principles of the circular economy, waste recycling, justifies the relevance of the chosen research area. The proposed program model is based on the use of trainable trees of systems (blocks) of fuzzy inference of the first and second types. This approach made it possible to avoid unnecessary complication of the bases of fuzzy inference rules when using only one fuzzy block when building a multi-parameter model of the entire multi-stage complex system. The use of several fuzzy inference blocks that describe the behavior of individual units of the system and their configuration in accordance with the physical structure of the system allows the use of relatively simple sets of rules for individual blocks. The joint selection of their parameters when training a tree of fuzzy blocks makes it possible to achieve high accuracy of the solutions obtained. The novelty of the research results is the proposed software fuzzy model of an integrated system for processing finely dispersed ore raw materials. The results of a simulation experiment conducted in the MatLab environment using a synthetic data set generated in Simulink are presented. The results showed that the trained fuzzy model provides good fidelity of the parameters and variables from the test part of the synthetic set.