Plant Operators Research Articles

Operators Need to Know How to Make Ethical Decisions

Understanding how ethics inform a water treatment plant operator's daily experience helps staff realize the significance of their efforts to protect public health and the environment.

The development of international civil liability legal regime for nuclear damage and their inspiration for domestic law-making

As International Atomic Energy Agency has stated in its Handbook on Nuclear Law, “Even in situations for which the highest standard of safety has been achieved, the occurrence of nuclear accidents cannot be completely excluded.” Therefore, the international legal framework for nuclear damage compensation liability has been evolving since the establishment of Nuclear Energy Agency of Organization for Economic Co-operation and Development (OECD NEA) and International Atomic Energy Agency (IAEA). Over the years, various international treaties have been enacted to address the compensation of nuclear damage and to establish liability regimes for nuclear incidents. To date, these treaties have established a series of legal principles of nuclear damage liability, such as the sole liability principle, the strict liability principle, the financial guarantee principle etc., which have been developing since establishment. This paper offers an overview of the historical development of the principles of these international treaties for nuclear damage liability and thus draws upon both primary and secondary sources, including treaties, official documents, academic literature, and reports by international organizations. Including the legislation study methodology, comparative methodology is also adopted in this paper to analyze the changes and trend of these principles. The paper reveals that the Paris Convention, which was established in 1960, was the first attempt to establish a comprehensive legal regime for nuclear damage liability. Most of the principles of this Convention have been inherited by subsequent international treaties and domestic legislations. With the awareness of protecting public’s rights having been significantly strengthened, the range of compensation has been broader, the matters of immunity from liability for operators of nuclear power plants have been reduced, the limitation of the compensation amount has been higher etc. In conclusion, the international legal regime for nuclear damage liability has been showing a shift from protecting the development of the nuclear industry to a joint protection of both public health and rights and the nuclear industry, which should be paid attention to and deeply learnt by domestic legislators of all states for the establishment and perfection of their domestic legislation in this field.

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

Protection of Nuclear Power Plants from Fraudulent Items

At present a lot of problems with fraudulent items occurred. They disrupt the safety of industry, power engineering and other objects that are important for the life of human society. The article gives infor-mation on causes of occurrence of fraudulent items in nuclear power plants and shows that protection against fraudulent items needs to perform during the whole supply chain of critical items. In detail, it concentrates to the quality of awarding terms of references, which nuclear power plant operator prepares when it gives order to commercial supplier and of process of take-over control of the deliver commodity. Due to national security, the safety of nuclear facilities is also supervised by national nuclear regulatory body. During our research we processed checklists by which national nuclear regulatory body can reveal risks in awarding the contracts to commercial suppliers and in take-over control, and ensures improve-ment of these processes.

Does the health of local populations modify occupational differences in employment rates of older workers? Findings from the ONS Longitudinal Study 2001–2011

Poor health is a key reason for early exit from the labour market. Few studies have explored how the health of local populations is related to occupational differences in employment outcomes among older people. Our study used data for England and Wales from the ONS Longitudinal Study linked with 2001 Census measures of the health of the older working age population at local authority level. We included 128,710 people aged 40-64y in 2001 who were in paid work in the previous five years. We investigated the associations of both occupation and area level with two employment outcomes ten years later (in 2011): i) in paid work or not; ii) economic activity (employed (reference), unemployed, retired, sick/disabled, other). People in elementary occupations were more likely to not be in paid work in 2011 compared to those in managerial occupations (RRR 1·55 [95%CI 1·47,1·64]). Compared to the healthiest third of local authority areas, being resident in the unhealthiest third was associated with greater likelihood of not being in paid employment ten years later (RRR 1·25 [95% CI 1·18,1·33]). While area level health was associated with employment outcomes for all major occupation groups, the gap between the healthiest and unhealthiest areas was most marked for skilled trades; process, plant and machine operatives; and elementary occupations. Occupational differences for the economic activity outcome were most marked for the sick/disabled category. Policies to improve the health of local populations may support retention and reduce occupational inequalities in employment rates of older workers.

Systematizing risk assessment and responses to combustible dust from explosive characteristics

A systematic approach to defining dust explosion risks is presented, utilizing key parameters: minimum ignition energy (MIE), minimum ignition temperature of dust clouds (MITC), minimum explosion concentration (MEC), maximum explosion overpressure (Pmax), and the dust explosion characteristic index (Kst). These parameters form the basis for robustly assessing combustible dust explosion risks. Drawing on experimental findings, a safety evaluation method empowering plant operators to directly assess the safety of dust-handling processes was devised, ensuring effective dust explosion prevention. Evaluation of three common dust samples (aluminium, polyethene, and lycopodium) revealed varying explosion risks. Through comparative analysis with existing literature, this study aligns risk definitions, proposing a scoring method for process safety controls to effectively mitigate dust cloud explosion risks.

Estimation of the Put Option Value for Solar Power Plant Operators Participating in the Renewable Portfolio Standard (RPS) Spot Market

Estimation of the Put Option Value for Solar Power Plant Operators Participating in the Renewable Portfolio Standard (RPS) Spot Market

Hydro-thermal Power Reserve Allocation Under Outage Uncertainty

In recent decades, the energy sector, namely energy markets have proved to be changing considerably. Most of these changes result in increasing volatility related to global political and environmental factors. This volatility affects all entities operating in the power sector. This paper focuses on the power plant operator’s perspective. First, the global context of the power sector is discussed and the power plant operator’s role is reviewed. Further, the problem of power reserve is discussed revealing the motivations of the presented work. The central contribution of this work lies in the formulation and development of a stochastic model of power reserve coupled with a mixed-integer programming scheduling model. In the results section, we show that the developed model allocates power reserve in an optimal way respecting the technical and economic parameters of the power plants as well as handling the stochastic nature of the power reserve allocation problem by minimizing the mean value of the sum of penalty for not delivering contracted power and lost opportunity.

Resilience Adaptation Through Risk Analysis for Wastewater Treatment Plant Operators in the Context of the European Union Resilience Directive

To combat new threats to critical infrastructure, the European Union enacted new regulations for their member states. With the directive on measures for a high common level of cybersecurity (NIS2) and the directive on critical infrastructure resilience (EU RCE), EU member states must identify critical infrastructure (CIs) and enable measures to reduce the risk of default in stress situations. The topic of resilience in urban water systems has already been of interest in previous research. However, there are still open questions. As it is a multidisciplinary term, understanding resilience and its adaptation into management systems is not an easy task for practitioners. This study will provide an overview of resilience within the framework of municipal wastewater treatment plants (WWTP) and show the current situation of existing implementation of safety and security regulations, taking Germany as an example. One of the main requirements of the EU RCE is a risk assessment (RA) for CIs. Until now, risk analysis for WWTP in research was mostly carried out for individual WWTP. By applying guidelines from the drinking water sector, this paper shows a possible methodology for a risk analysis. This paper aims to support practitioners by forming a common understanding of resilience and risk as well as providing an example for a risk analysis.

An operational risk assessment method for petrochemical plants based on deep learning

The petrochemical industry is an important guarantee for the development of people’s lives, and the operational risk assessment method of personnel in the operation of petrochemical enterprises is the most important. Based on a deep learning algorithm, a new method based on the micro-Doppler effect and fuzzy analytic hierarchy process is proposed to evaluate the operational risk of personnel in petrochemical enterprises. The original monitoring image of petrochemical equipment is invoked, and micro-Doppler analysis is performed based on the original image to identify the activity target of the personnel on the job site and generate the activity map of the petrochemical plant operators. Based on the analysis data of the micro-Doppler effect, the fuzzy function and hierarchical analysis method are combined. The fuzzy theory is introduced into the analytic hierarchy process, and the inherent expert evaluation scores are transformed into fuzzy numbers. It makes the expert evaluation of petrochemical plants more accurate in the subsequent coupling and improves the objectivity of the traditional analytic hierarchy process. The scheme proposed in this paper can monitor real-time operation safety and provide a guarantee for the personal safety of field operators. This method plays an important role in improving the safety of petrochemical plants.

Incidence of type 2 diabetes by occupation – 10-year follow-up of the Gutenberg Health Study

Abstract Background In order to implement targeted type 2 diabetes (T2D) prevention measures for the workplace, it is important to identify high-risk occupations. Most previous studies examine the association between T2D and occupation cross-sectionally, but longitudinal studies are missing. This study aims to investigate the 10-year incidence of T2D in the working population of a German cohort and identify occupations with an increased risk. Methods Data from the population-based Gutenberg Health Study (GHS) were used to examine occupation at baseline (2007-2012) and T2D incidence at the 10-year follow-up. Occupational phases were coded into categories according to the KldB 2010. T2D was defined as either a physician-diagnosed T2D, HbA1c-level ≥6.5% or use of antidiabetic drugs. Age- and sex-standardised incidence and 95% confidence intervals (CI) were calculated for occupational groups based on the age- and sex-distribution of the German population. Standardised incidence ratios (SIR) and 95% CI were calculated using the incidence in the total GHS working population as the reference. Results Of 8130 workers at baseline, 45.2% were female, the mean age was 48.4 years and 4.5% had prevalent T2D. 388 cases occurred between baseline and the 10-year follow-up within the subgroup of persons without prevalent T2D at baseline and with follow-up data (n = 5954). The standardised incidence was 6.9%. “Food production and processing” (20.7%), “cleaners” (16.5%) and “drivers and mobile plant operators” (14.8%) had the highest standardised incidences. Compared to the total working population, we observed a threefold increased SIR for employees in “food production and processing” (3.0, 95% CI 1.8-4.7) and a two times higher SIR for “cleaners” (2.2, 95% CI 1.0-4.7) and “drivers and mobile plant operators” (2.2, 95% CI 1.3-3.3). Conclusions We identified occupations with an increased risk of T2D. Preventive measures and etiological research on work-related risks should focus on these occupations. Key messages • The highest standardised incidence was observed in the occupational groups “food production and processing”, “cleaners” and “drivers and mobile plant operators”. • Targeted prevention measures for type 2 diabetes in the workplace should focus on these occupational groups with increased incidence.

Optimal Dispatching Strategy for Textile-Based Virtual Power Plants Participating in GridLoad Interactions Driven by Energy Price

The electricity consumption of the textile industry accounts for 2.12% of the total electricity consumption in society, making it one of the high-energy-consuming industries in China. The textile industry requires the use of a large amount of industrial steam at various temperatures during production processes, making its dispatch and operation more complex compared to conventional electricity–heat integrated energy systems. As an important demand-side management platform connecting the grid with distributed resources, a virtual power plant can aggregate textile industry users through an operator, regulating their energy consumption behavior and enhancing demand-side management efficiency. To effectively address the challenges in load regulation for textile industry users, this paper proposes a coordinated optimization dispatching method for electricity–steam virtual-based power plants focused on textile industrial parks. On one hand, targeting the impact of different energy prices on the energy usage behavior of textile industry users, an optimization dispatching model is established where the upper level consists of virtual power plant operators setting energy prices, and the lower level involves multiple textile industry users adjusting their purchase and sale strategies and changing their own energy usage behaviors accordingly. On the other hand, taking into account the energy consumption characteristics of steam, it is possible to optimize the production and storage behaviors of textile industry users during off-peak electricity periods in the power market. Through this electricity–steam optimization dispatching model, the virtual power plant operator’s revenue is maximized while the operating costs for textile industry users are minimized. Case study analyses demonstrate that this strategy can effectively enhance the overall economic benefits of the virtual power plant.

Using Artificial Neural Networks to Predict Operational Parameters of a Drinking Water Treatment Plant (DWTP)

The scope of the present study is the estimation of key operational parameters of a drinking water treatment plant (DWTP), particularly the dosages of treatment chemicals, using artificial neural networks (ANNs) based on measurable in situ data. The case study consists of the Aposelemis DWTP, where the plant operator had an estimation of the ANN output parameters for the required dosages of water treatment chemicals based on observed water quality and other operational parameters at the time. The estimated DWTP main operational parameters included residual ozone (O3) and dosages of the chemicals used: anionic polyelectrolyte (ANPE), poly-aluminum chloride hydroxide sulfate (PACl), and chlorine gas (Cl2(g)). Daily measurable results of water sample analysis and recordings from the DWTP Supervisory Control and Data Acquisition System (SCADA), covering a period of 38 months, were used as input parameters for the artificial neural network (1188 values for each of the 14 measurable parameters). These input parameters included: raw water supply (Q), raw water turbidity (T1), treated water turbidity (T2), treated water residual free chlorine (Cl2), treated water concentration of residual aluminum (Al), filtration bed inlet water turbidity (T3), daily difference in water height in reservoir (∆H), raw water pH (pH1), treated water pH (pH2), and daily consumption of DWTP electricity (El). Output/target parameters were: residual O3 after ozonation (O3), anionic polyelectrolyte (ANPE), poly-aluminum chloride hydroxide sulfate (PACl), and chlorine gas supply (Cl2(g)). A total of 304 different ANN models were tested, based on the best test performance (tperf) indicator. The one with the optimum performance indicator was selected. The scenario finally chosen was the one with 100 neural networks, 100 nodes, 42 hidden nodes, 10 inputs, and 4 outputs. This ANN model achieved excellent simulation results based on the best testing performance indicator, which suggests that ANNs are potentially useful tools for the prediction of a DWTP’s main operational parameters. Further research could explore the prediction of water chemicals used in a DWTP by using ANNs with a smaller number of operational parameters to ensure greater flexibility, without prohibitively reducing the reliability of the prediction model. This could prove useful in cases with a much higher sample size, given the data-demanding nature of ANNs.

A new approach on the management of landfill leachate reverse osmosis concentrate: Solar distillation coupled with struvite recovery and biological treatment

The management of reverse osmosis (RO) concentrate remains a challenging task for operators of Landfill Leachates Treatment Plants. In this article we suggest an integrated treatment scheme for RO concentrate that combines solar distillation, struvite precipitation to reduce ammonia content of the distillate and biological treatment of the supernatant either with mixed cultures of bacteria or with microalgae. Experiments in a pilot-scale solar still, equipped with underfloor heating system, showed that the production rate of the distillate ranged up to 3.17 L/d m2. The distillate was characterized by elevated average concentrations of ammonium nitrogen; 2028 mg/L and 1358 mg/L in the two experiments conducted, respectively. A decreasing trend on concentrations of NH4+-N was noticed during these experiments, while the opposite was observed for COD. Struvite recovery experiments showed that the optimum Mg:NH4:PO3 ratio was that of 2:1:5.8. Under these conditions, the NH4+-N removal reached 88%. Further treatment of the process supernatant into a 4-L hybrid sequencing batch reactor with biocarriers and activated sludge achieved NH4+-N removal higher than 98% in Phases C and D, where 450 and 600 mL of supernatant were added, respectively. Similar removal was also observed in a 2-L bioreactor with microalgae Chlorella sorokiniana when 150 mL of struvite supernatant were added (Phase B) while further increase of the amount of added supernatant to 200 mL resulted to a sharp stop of NH4+-N consumption (Phase C). Calculations for a landfill serving 20,000 inhabitants and a daily RO concentrate production of 6 m3/d showed that the required area for the construction of the solar still was 1893 m2 and the volumes of the hybrid and the microalgae reactor were 54 m3 and 60 m3, respectively. The recovered solid material of struvite process, after characterization for heavy metals and organic micropollutants, could be reused to the fertilizers industry.

APT: Alarm Prediction Transformer

Distributed control systems (DCS) are essential to operate complex industrial processes. A major part of a DCS is the alarm system, which helps plant operators to keep the processes stable and safe. Alarms are defined as threshold values on individual signals taking into account minimum reaction time of the human operator. In reality, however, alarms are often noisy and overwhelming, and thus can be easily overlooked by the operators. Early alarm prediction can give the operator more time to react and introduce corrective actions to avoid downtime and negative impact on human safety and the environment. In this context, we introduce Alarm Prediction Transformer (APT), a multimodal Transformer-based machine learning model for early alarm prediction based on the combination of recent events and signal data. Specifically, we propose two novel fusion strategies and three methods of label encoding with various levels of granularity. Given a window of several minutes of event logs and signal data, our model predicts whether an alarm is going to be triggered after a few minutes and, if yes, it also predicts its location. Our experiments on two novel real industrial plant data sets and a simulated data set show that the model is capable of predicting alarms with the given horizon and that our proposed fusion technique combining inputs from different modalities, i. e. events and signals, yields more accurate results than any of the modalities alone or conventional fusion techniques.

Thermal Power Plant Performance Analysis by Estimating Boiler Efficiency via Indirect Method: A Case Study

The performance of power plants is very critical since it is directly related with operating and electricity production costs. Among the other different type of power plants, coal-fired ones have advantages such as reliability and low cost fuel. In this paper, a coal (lignite) fired thermal power plant having capacity of 160 MW is taken into consideration and the impact of condenser pressure, moisture content of lignite, excess air coefficient, efficiency of turbine pressure and heater numbers on power plant thermal efficiency is investigated. It is aimed to determine performance losses of each equipment by means of the thermodynamics and economic analysis. In this scope, it is expected that the power plant operator will be able to evaluate the reduction potential of equipment performance losses and ensure more effective use of the power plants by correctly planning the maintenance and rehabilitation needs and times. In the calculations, the boiler efficiency was determined with EN 12952-15 standard (indirect method) since this method has higher accuracy in coal fired boilers. It is seen that the condenser pressure and excess air coefficient increments have not significant impact on power plant efficiency compared to moisture content of lignite, excess air coefficient, efficiency of turbine pressure and heater numbers. The significant effect is observed for fuel moisture content which rises from 22% to 47% and the power plant efficiency falls from 40% to 28%. The variation of the power plant thermal efficiency in case of failure of heaters is investigated and the power plant efficiency has decreased from 40.17% to 36.09% when the pre-heaters are no longer in to be in use because of any reason. In addition, revenue losses are estimated for each main equipment efficiency reduction for better use of power plant capacities and electricity lowering production costs.

Determining optimal component configurations for flexible biogas plants based on power prices of 2020–2022 and the legislation framework in Germany

The aim of this study is to find optimal component configurations for flexible biogas plants considering electricity prices in Germany, for maximum annuities considering costs and revenues. Cases for solely power-price driven operation and cogeneration-led operation were investigated for electricity prices in 2020, 2021, and 2022 in hourly resolution.Additional revenue from flexible operation increases as price volatility increased from 2020 to 2022. Interestingly, the choice of capacity for the combined heat and power unit (CHPU) is determined by the expected service life of the CHPU. Irrespective of electricity market signals, power quotients (PQ) of at least PQ = 4 avoid reinvestment for the CHPU within 20 years.Another result is that the the optimality region for CHPU capacity flattens for 2022 and shift to capacities above PQ = 4. Thus, high price volatility will allow plant operators to amortise even larger CHPUs, which could stimulate the installation of peak load by flexible biogas plants. Considering the day-ahead market can provide clear recommendations for optimal component configurations.The presented Smart-Force-Optimisation (SFO) combines a prefiltering of schedules matching given constraints and a brute force optimisation to the filtered subset. Thus, SFO has an improved performance comparing to bare brute force approaches.

Research on Assessment Method for Cognitive Influence Factors of Nuclear Power Plant Operators Based on Fuzzy Analytic Hierarchy Process and Deep Learning of Electroencephalogram Signals

This study explores the factors influencing the cognitive processes of operators in digital nuclear power plants, with a focus on the correlation between these factors and electroencephalogram (EEG) features. Initially, based on expert consultations, seven factors were considered: stress, time, fatigue, procedural complexity, user interface experience, procedural clarity, and efficiency. From these, four were identified as the most crucial for each stage of the cognitive process, highlighting their significant roles in influencing cognitive performance and potentially correlating with distinct EEG characteristics. These were assessed using the fuzzy analytic hierarchy process (FAHP) to determine the weightings of influences across the cognitive stages of monitoring, decision making, and execution. Employing a simulated scenario of a steam generator tube rupture, subjective questionnaires were utilized to gauge participant perceptions of influencer impacts at each stage, calculating human factors fuzzy synthetic values. Concurrently, EEG signals were segmented by operational steps, extracting around 114 features across the time, frequency, and time-frequency domains, which were then dimensionally reduced to 17 principal components via adaptive principal components analysis (APCA). A correlation analysis was performed between the human factors fuzzy synthetic values and the APCA-reduced EEG features of participants. Subsequently, the EEG feature columns of the eight selected participants were used as inputs to construct a transformer-based self-attention network model to evaluate the participants’ human factors fuzzy comprehensive values. The findings confirm the transformer model’s efficacy in assessing these values, evidencing a significant correlation between the EEG features and human factors fuzzy synthetic values. Integrating FAHP with machine learning methodologies, this model proficiently estimated operators’ cognitive states during various cognitive processes, significantly enhancing human-machine interface design and the operational safety and efficiency at nuclear power plants.

A nutrient circular economy framework for wastewater treatment plants

Given the criticality of phosphorous and the harm eutrophication causes, wastewater treatment plants (WWTP) are undergoing circular economy (CE) conversions into becoming centres for resource recovery. This framework paper identifies the environmental, societal, political, commercial, economic, consumer, regulatory, legal, infrastructural, technological, international compliance, academic, agricultural, and plant-operator factors affecting the micro, macro and meso success and failure of nutrient CE WWTP across scope classifications. From this, nutrient CE framework is not as simplistic as it looks, given the multitude of impingers that need to be considered. The lack of technological readiness and vast selection methodologies for some nutrient recovery technologies, low private-sector investment, divisive consumer perceptions, unique economic situations, localised regulatory specifications, threats of inefficient technology lock-ins, poor acceptance among farmers and WWTP operators due to infrastructural incompatibilities, lack of efficient CE supply chains, legacy infrastructure, poor digitised solutions for nutrient CE management, low training and awareness, and integration of nutrient CE WWTPs with broader society are barriers. On the contrary, nutrient CE WWTP will mainly be driven by regulations, subsidies and consumer-WWTP-farmer nutrient CE acceptances. This framework aims to tie in and provide solutions to overcoming barriers to nutrient CE WWTPs that is universally applicable. Consequently, the framework provides a holistic structure for policy, legislative, academic, agricultural, plant operators, commercial and consumer stakeholders a roadmap for the success and difficulties of nutrient CE WWTP adoption.

Optimization of Chemical Engineering Processes in the Mining and Metal Industry: A Review

Process optimization is acutely vital: mining and a metal industries research. Through applying modelling and optimization tools, businesses see tremendous improvement in process efficiencies, declining costs of production, and ensuring high quality. The valuable of simulation tools have also been reflected in the design of the virtual plants which make possible to conduct test runs of different process scenarios thereby providing the opportunity to optimize the performance of the plants. So, even if these issues are close to being resolved, some challenges must still be tackled to approach the full field of process optimization enjoyment. One of the main drawbacks is the integration of sustainable development and environmental evaluation into optimization phase. This can be achieved by developing models that are amended to an environmental impact of each process parameters and afterwards an optimization of processes will be based on environment factors. The second challenge is for the research community to come up with more advanced and sophisticated mathematical models that are capable of not only capturing but also depicting the diverse and complex interactions that take place between different processes variables. Such an approach indeed requires implementation of more precise data analysis tools and techniques, including neural networks and genetic algorithms. While there are many difficulties in it way, the process optimization of a bright future is foreseeable. The fast growth of the technological sphere, such as machine-2-machine and big data analytics, enables process optimization which was unavailable before. By implementation of a set of sensors and real-time data analysis plant operators are being provided with necessary information that enables this way to exercise real-time decisions for the sake of the increase of the plant performance. Minimizing energy consumption, increasing the product yield, and less damaging to the environment is a potential focus for the research. Through the application of mathematical models, optimization algorithms, and simulation-based approaches, one can compare the different methods of each process and the produced product yields.