Efficient Supply Research Articles

CO2 microbubbling system for lithium carbonate precipitation

Lithium carbonate (Li2CO3) is becoming increasingly valuable with the growth of the lithium-ion battery industry. For effective Li2CO3 production, the efficient supply of the carbonate source to the solution is crucial. Conventionally, sodium carbonate has been used as the carbonate source; however, the importance of CO2 utilization has been recently recognized. Conventional CO2-based carbonate sourcing exhibits limited efficiency. In this study, we propose a CO2 microbubbling system based on a rotating nozzle for Li2CO3 precipitation. The CO2 micro-bubbling system demonstrated a 1.6 to 1.9 times greater precipitation reaction rate than that of the conventional CO2 bubbling system. This rate enhancement could be attributed to the increased gas-liquid contact surface and greater residence time due to the generation of CO2 microbubbles. Additionally, the Li2CO3 reaction terminated when the pH of the solution dropped below 9.4–9.7 approximately. The re-dissolution of precipitated Li2CO3 occurred below this pH range as bicarbonate ions became the dominant species over carbonate ions. This study successfully demonstrated that CO2 microbubbling technology could be an excellent alternative for carbonate sourcing systems in the Li2CO3 production industry.

Accounting information sharing within buyer-supplier collaborations: Insights from a developing country

Background: The significance of the context, or the environment, is pivotal in influencing the practices of management accounting.Objectives: This article aims to explore the significance, methods and challenges of accounting information exchange in buyer–supplier collaborations.Method: In buyer–supplier collaborations, data are gathered from 13 semi-structured interviews and 250 questionnaires. The analysis is conducted using SPSS version 25, employing non-parametric Kruskal–Wallis test and content analysis techniques.Results: Respondents recognise its benefits but prefer informal communication. Challenges such as diverse systems and mistrust hinder formal sharing. Despite obstacles, informal channels remain effective, although concerns about data leakage persist, leading to a conservative approach.Conclusion: In the Egyptian textile supply chain, members prioritise individual goals over collective ones. Informal methods, such as backdoor access, facilitate accounting information sharing among buyer–supplier partnerships. Formal sharing protocols are lacking, with limited one-way technical data exchange. Trust issues and data security concerns persist. Despite the absence of formal disclosure, cost estimations remain feasible. The company adopts a conservative approach, limiting access to records in most cases.Contribution: In essence, the article not only sheds light on the complexities and nuances surrounding accounting information sharing in the textile supply chain but also emphasises the resilience of informal communication channels in overcoming challenges. As the industry navigates these dynamics, recognising the importance of relational ties and informal exchanges could be pivotal in fostering a more efficient and collaborative supply chain ecosystem.

Supply chain management (SCM) framework for small and medium manufacturing enterprises (SMEs) in Ghana

Efficient supply chain management is essential for small and medium-sized enterprises (SMEs) in Ghana to guarantee a dependable flow of products and services, sustain competitiveness, and promote growth and job creation. The objective of this study is to analyse the current supply chain models utilised by manufacturing small and medium-sized enterprises (SMEs) in Ghana. Additionally, it intends to propose a prototype model that will aid manufacturing SMEs in achieving sustainability, growth, and future expansion in Ghana and other regions. The Conceptual framework was developed by examining variables identified in the existing literature on supplier management practices in small and medium-sized enterprises (SMEs), as well as inventory management approaches that are relevant to SMEs. A structured questionnaire was employed to collect data from the main source. This study employs a mixed method approach, which includes the participation of 363 respondents. Interviews and surveys were carried out with small and medium-sized enterprise (SME) owners and managers. The data was analysed using the SPSS (v 27.0) computer application, which involved both inferential and descriptive statistical analysis. The findings revealed essential components of the prototype, such as the implementation of supply management (SM), proficient information management (IM), streamlined distribution strategies, and strong marketing and sales strategies, in addition to customer relationship management (CRM) and effective post-sales services. Implementing these supply chain management (SCM) principles effectively can guarantee the sustainability, growth, employment generation, and overall performance of small and medium-sized enterprises (SMEs) in Ghana.

Effectiveness of Halal Traceability and Self-Declared Certification on Indonesian MSMEs Performance

This study aims to examine in depth the effectiveness of halal tracea-bility in Indonesia and identify the impact of self-declared halal certifi-cation on the performance of small and medium-sized enterprises (SMEs) in the country. The research method used is qualitative, which is reinforced by statistical analysis through survey data from the Popu-lix application as well as a comprehensive literature review. Data anal-ysis was conducted through various techniques, including regression tests, correlation tests, chi-square tests, and content analysis. The re-search findings confirm that the effectiveness of the halal tracking sys-tem significantly contributes to improving the resilience and competi-tiveness of MSMEs. Moreover, self-declared halal certification has been proven to increase consumer confidence in halal products and their integrity. This research makes an important contribution by high-lighting the urgency of integrating efficient halal supply chain man-agement practices, including reliable tracking systems and credible cer-tification. Furthermore, this research underscores the necessity of en-hancing consumer awareness of halal products, thereby facilitating in-formed purchasing decisions and fostering the growth of a more resili-ent halal ecosystem in Indonesia.

Exceptional High‐Performance Oxygen Transport Membrane and Comprehensive Study on Mass/Charge Transport Properties

This study focuses on mixed‐conducting perovskite membranes for efficient oxygen supply, aiming to replace energy‐intensive cryogenic distillation with a more practical alternative. A La and Nb co‐doped BaCoO3−δ perovskite is introduced, Ba0.95La0.05Co0.8Fe0.12Nb0.08O3−δ (BLCFN) with a record‐breaking oxygen permeation flux, surpassing all known single‐phase perovskite membranes. To elucidate its superior membrane performance, the mass/charge transport properties and equilibrium bulk properties are investigated and quantitative indicators (DO = 5.8 × 10−6 cm2 s−1, kO = 1.0 × 10−4 cm s−1, σion = 0.93 S cm−1 at 900 °C) reveal fast diffusion and excellent surface gas‐exchange kinetics. The oxygen permeability of 12.4 mL cm−2 min−1 and over 200 h of long‐term stability is achieved in an air/He atmosphere at 900 °C. By presenting a material that demonstrates higher performance than Ba0.5Sr0.5Co0.8Fe0.2O3−δ (BSCF), currently known for its highest permeability, it is believed that this marks a significant step toward innovative performance enhancement of perovskite oxide‐based membranes.

A novel generation design dipole magnet power supply of booster to storage ring transport line in NSRRC

The Taiwan Light Source (TLS) is a synchrotron radiation facility located in Taiwan. The booster ring at TLS injects electrons into the storage ring, which then circulates the electrons to produce synchrotron radiation for various experiments. The Taiwan Light Source (TLS) has been in service for more than 20 years, and some of the power supplies used in the facility are old and hard to maintain repair and the component are phase-out. The upgrade is specifically aimed at replacing the power supply for the BTS (Booster to Storage ring) transmission line with a new machine. During the regular shunt down time, dipole magnet power supply replacements were completed. The main reason for using the Chroma 62075H-30E power supply is commonality, for this machine is widely used in Taiwan Photon Source (TPS), and the output current of the power supply is paralleled to a dipole magnet. Therefore, a total of 17 power supplies are used in this upgrade planned to be executed. This novel generation upgrade design is important because the power supply for the BTS transmission line is critical for the operation of the facility, as it is responsible for maintaining the electron beam at a stable energy level as it is injected from the booster ring into the storage ring. A more reliable and efficient power supply can help improve the stability and quality of the electron beam, leading to better experimental results and more efficient use of the facility. Overall, this novel generation upgrade design is part of the ongoing effort to modernize and improve the infrastructure of the TLS, ensuring that it remains a state-of-the-art research facility for years to come.

Optimizing smart grid performance: A stochastic approach to renewable energy integration

Smart grids offer numerous possibilities for developing and executing sustainable and efficient electricity supply chains that exhibit increased resilience to disturbances. The exploration of the Electric Supply Chain Network (ESCND) design problem using smart grids has been limited. This article aims to tackle this challenge through the application of a robust multi-objective optimization approach, encompassing three economic goals (maximizing profit), environmental goals (minimizing greenhouse gas emissions), and resilience goals (maximizing network resilience). Additionally, the optimization process takes into account the dual objectives of maximizing efficiency and minimizing energy loss by incorporating relevant cost considerations. The proposed methodology incorporates distinctive features of smart grids, such as demandside management programs, microgrid structures, bidirectional distribution lines, and consumer–supplier nodes. It addresses various interconnected decisions, including location placement, capacity expansion, load allocation, and pricing. To solve the formulated model, a potent combination of multi-objective optimization methods based on cutting-plane algorithms and AUGMECON2 is introduced. Subsequently, the proposed model and solution approach are applied to a practical case study, and the obtained results are comprehensively examined. The findings suggest that, despite potential contradictions between economic and environmental objectives, the implementation of smart grids can concurrently improve environmental performance and network flexibility.

Sustainability of healthcare supply chain management: a panoramic approach through SLR and bibliometric

PurposeThe healthcare sector increasingly recognizes how critical sustainable supply chains are to lowering environmental impact, boosting productivity and satisfying public expectations for morally and responsibly provided healthcare. Consequently, the current study aims to thoroughly evaluate the literature on the sustainability of healthcare supply chain management.Design/methodology/approachThe paper uses a systematic literature review (SLR) technique and bibliometric review to examine the benefactions of different authors, nations and organizations to healthcare sustainability through bibliometric and network analyses.FindingsThe study concludes that the healthcare industry may advance sustainability on all levels by incorporating technology into the fundamentals of sustainability. Patient care is given priority in this proposed approach, which can also help healthcare executives create strategies that support efficient healthcare supply chains.Research limitations/implicationsThe research study can serve as a basis for future investigations into additional healthcare management domains, where integrating a sustainable supply chain can yield superior and observable results and bridge deficiencies in management protocols.Originality/valueUsing bibliometric visualization, this study shows the relevance of sustainability in the healthcare supply chain. By identifying its advantages, present-day circumstances, applications and prospective future research fields, the study took up the review and relevance of sustainability in many parts of the healthcare industry.

Supply Chain Management Enhance NG Efficiency and Collaboration Industry Company

Purpose Collaboration may be the most important core competency and has been described as the engine of efficient supply chain management. There is a common perception, nevertheless, that not many businesses have fully realised its potential. In order to determine best practices and evaluate the state of supply chain collaboration, a research was conducted. Design, procedure, and strategy CEOs of supply chains offered advice on working together. A conceptual model characterising the behaviour, culture, and interpersonal relationships linked to productive collaboration was created using survey data, in-person interviews, and an analysis of the literature on collaboration Results. The increase of efficiency, effectiveness, and market positioning for the enterprises of the responders are positive results associated to collaboration. Research constraints and implications Although the limited sample size is a drawback, it is counterbalanced by the calibre of the respondent pool and their level of training and experience. Getting feedback from only one partner in the cooperative partnerships is another drawback. It would be possible to ascertain how collaboration-related interactions and variables evolve over time by creating a longitudinal research. Consequences for practice A "blurring of lines" between organisations was highlighted by a number of respondents in contrast to a "us vs. them" mentality. This was conveyed in several ways, including by recognising the agreements as co-owned, treating them as a single unit, and reorienting the attention to the greatest possible compromise. Even though many of the respondents said that benefits are not given out equally, they acknowledged that they benefit enough from the cooperative agreements to make them worthwhile. Uniqueness and worth Many of today's leading firms are mentioned in real-world practical experiences.

Comparative Analysis of Different Burner Concepts in a Locally Manufactured Bread-Baking Oven

The supply and control of heat within an oven with minimal loss is an integral process in optimizing the bread-baking process. Research over the years reports on measures to control heat, however, there is limited data on optimizing burner designs for efficient heat supply. Challenged with enhancing the functionality of a bread baking oven made locally, three different burner concepts (i.e., U, H and Rectangular shaped) were designed, fabricated and evaluated in a locally manufactured bread baking oven. The study investigated the three burner configurations to enhance the locally manufactured ovens' environmental sustainability, cost-effectiveness, and efficiency. The study also assessed each burner design concept's performance in relation to heat distribution, fuel consumption, and emissions through thorough experimentation and analysis. Thermocouples were used to determine the temperature differences within the oven and outside the oven walls to verify heat losses. The results showed that the concept burner design can be used to bake bread with good quality parameters like colour, texture and taste within efficient baking time. Computational Fluid Dynamic (CFD) analysis performed on the proposed burner design concepts in relation to heat flow show that continuous flow of heat was assured during baking. Also, simulation performed on the baking trays show an acceptable stress and strain levels as well as favorable factor of safety, indicating that the designs proposed is suitable for the purpose. Data analysis performed on the heat generated within the oven chamber considering the lower and upper trays for all the burners evaluated can be ranked in terms of percentage as RB > HB > UB (41.39% > 30.72% > 27.89%). Based on the study conducted, the authors can suggest the best design concept for heat generation in locally manufactured ovens should be rectangular-shaped.

ПЕРСПЕКТИВИ ЗАСТОСУВАННЯ РЕКУРЕНТНОГО АНАЛІЗУ ДЛЯ ЦИКЛІЧНИХ ПРОЦЕСІВ У ВИРОБНИЦТВІ ЕЛЕКТРОЕНЕРГІЇ

The increasing demand for flexible and dynamic power systems drives the search for new modeling and forecasting methods. Recurrent analysis, which has gained wide recognition in natural language processing and machine learning, has the potential to become a promising tool for research and optimization of cyclic processes in electricity generation. This paper explores the potential applications of recurrent analysis for addressing various energy-related tasks including electricity demand forecasting, dispatch optimization, power plant scheduling, and consumption data analysis. The paper discusses the advantages of recurrent analysis, such as flexibility, learning ability, and high accuracy, as well as the challenges associated with large data volumes, computational resources, and result interpretation. It emphasizes the prospects of recurrent analysis for developing more efficient, reliable, and sustainable power systems. The article also explores the integration of recurrence analysis with other data-driven techniques such as machine learning and statistical modeling. This integration enhances the predictive capabilities of recurrence analysis, allowing for more accurate and reliable forecasts of energy production and demand cycles. The synergy between recurrence analysis and machine learning algorithms can lead to the development of advanced control systems that dynamically adjust to changing conditions in real-time, thus ensuring a more stable and efficient power supply. In conclusion, the article posits that recurrence analysis holds significant potential for enhancing the understanding and management of cyclic processes in power generation. By providing detailed insights into the temporal dynamics of energy production systems, recurrence analysis can contribute to more efficient and reliable power generation, ultimately supporting the transition to sustainable energy systems. The adoption of recurrence analysis in the energy sector represents a step forward in leveraging advanced mathematical techniques to address the complexities of modern power generation.

Supply chain network design based on Big Data Analytics: heuristic-simulation method in a pharmaceutical case study

Digital technology is a pivotal aspect of the ongoing digital transformation upon which supply chain networks (SCNs) heavily rely. Through the utilization of Big Data Analytics (BDA), organizations can gain insights into sales, production and formulate decision support systems. The pharmaceutical supply chain emerges as a crucial player in producing and distributing medications vital for treating COVID-19 patients. In this study, we have innovatively designed a multi-echelon Pharmaceutical Supply Chain Network (PSCN) using BDA during the COVID-19 pandemic. We have developed a novel Mixed-Integer Non-Linear Programming model addressing allocation, production planning, and inventory control decisions, known as the LAPI problem. A transformation method is employed to convert the non-linear model into a linear one to enhance computational efficiency. The primary objective of our mathematical model is to minimize the total cost and CO2 emissions of the transportation system and established centres while maximizing the shelf life of medicines. Our experiments reveal that medium- and large-scale problems, characterized by the three main attributes of BDA – variety, velocity and volume (3 V‟s), pose challenges for optimal solutions. We present a case study of Iran to validate the mathematical model for large-scale problems. Employing a simulation approach, we estimate the demand for required pharmaceuticals, underscoring our proposed model’s real-world applicability and effectiveness. This research contributes to the growing necessity of eco-friendly and efficient supply chain strategies, especially in the critical domain of pharmaceuticals during pandemic situations.

Consumer service level-oriented resilience optimisation for e-commerce supply chain considering hybrid strategies with blockchain adoption

ABSTRACT The booming development of customised e-commerce makes e-commerce supply chains experience a severe test of consumer service level (CSL). An efficient e-commerce supply chain resilience optimisation method addressing economic performance and service performance is proposed to improve customised services under disruption risks. The proposed method includes a hybrid strategy considering both a resistance strategy with blockchain (BCT) adoption and time-dependent recovery strategies simultaneously. A two-stage multi-period multi-product stochastic programming with BCT adoption is then proposed, which (1) implements BCT as the resistance strategy in the pre-disruption stage; (2) collaborates four recovery strategies in the post-disruption stage; (3) supports the e-tailer in making decisions and optimises his profit and order fulfillment time while taking product priorities into account. Using the actual data of Chinese e-commerce during the public health emergency in 2020, it is demonstrated that (1) the applicability of the model with BCT adoption in both supply chain resilience and CSL improvement; (2) the performance of the hybrid strategy in long-term disruption management; (3) the efficiency and robustness of the proposed solution approach for multi-objective high-dimensional stochastic programming problems. E-tailers can optimise decision-making under long-term disruptions by the proposed methodology in practices.

Supply Chain Optimization in the Package Industry through Machine Learning Analysis

The package industry relies heavily on efficient supply chain management to meet customer demands and maintain profitability. However, managing complex supply chains involving multiple suppliers, transportation networks, and distribution channels poses significant challenges. This research proposes a machine learning-based approach to optimize supply chain operations in the package industry. By analysing historical data on supply chain activities, including procurement, inventory management, and distribution, our system aims to identify patterns and trends to improve decision-making processes. Machine learning algorithms such as support vector machine, naive Bayes, and logistic regression are utilized to forecast demand, optimize inventory levels, and streamline logistics operations. Experimental results demonstrate the effectiveness of the proposed approach in enhancing supply chain efficiency and reducing operational costs in the package industry.

Single cell protein production from methane in a gas-delivery membrane bioreactor

Single cell protein (SCP, or microbial protein) is one of the emerging alternative protein sources to address the global challenge of food insecurity. Recently, the SCP produced from methane has attracted substantial attention since methane is a renewable resource attainable from anaerobic digestion. However, the supply of methane, an insoluble gas in water, is one of the major challenges in producing methane-based SCP. This work developed a novel bioreactor configuration, in which hollow fiber membrane was used for efficient methane supply while microorganisms were growing in the suspended form favourable for the biomass harvest. Over a 312-day operation, the impacts of three critical parameters on the SCP production were investigated, including the ratio of methane loading to ammonium loading, the ratio of methane loading to oxygen loading, and the sludge retention time (SRT). Under the condition of 4 g CH4/g NH4+, 4 g O2/g CH4, and SRT of 4 days, the highest SCP production yield was observed and determined to be 1.36 g SCP/g CH4 and 5.05 g SCP/g N, respectively. The protein content was up to 67 %, which is higher than the majority of reported values to date. Moreover, the methane and ammonium utilization efficiencies were both close to 100 %, suggesting the highly efficient utilization of substrates in this new bioreactor configuration. A high relative abundance of essential amino acids (EAA) above 42 % was achieved, representing the highest EAA content reported. These findings provide valuable insights into SCP production using methane as a feedstock.

Agriyug: A Python-Based Approach to ECommerce in Agriculture

This research explores the evolving landscape of e-commerce, particularly in the context of agricultural product transactions. With a growing trend in online shopping, users increasingly seek a seamless and enjoyable experience, specifically in the domain of purchasing agricultural goods. This study delves into the transformative role of Python in shaping the infrastructure of e-commerce for agricultural products. The research initiative, labelled as "AgriYug," aims to provide valuable insights into the changing dynamics and prospects of agricultural e-commerce. The focus is on enhancing the online shopping experience for farm products by leveraging the capabilities of Python technology. This research outlines the development of an e-commerce platform specifically designed to empower Indian farmers by enabling direct sales to consumers without intermediary intervention. Aligned with the national sentiment recognizing farmers as the backbone of the country, the platform addresses the economic challenges faced by farmers due to middlemen in the supply chain. The objective is to enhance farmers' economic autonomy and provide consumers with affordable, fresh produce. This initiative contributes to the broader goal of optimizing the agricultural market by eliminating excessive intermediary fees, ensuring fair compensation for farmers, and creating a more efficient supply chain.

Analysis and Design of Water Distribution Network for Jabalpur Cantonment Board Area

In this research paper focuses on studying and improving the water distribution network in the Jabalpur Cantonment Board Area. Water distribution networks are crucial infrastructures for ensuring reliable water supply to communities. However, many areas, including Jabalpur Cantonment, face challenges related to water distribution efficiency, reliability, and equitable access. The study begins with a comprehensive analysis of the existing water distribution network in the Jabalpur Cantonment Board Area. Factors such as network layout, pipe material, age of infrastructure, and water demand patterns are thoroughly examined to identify existing inefficiencies and areas for improvement. Using advanced engineering tools such as WaterGems and methodologies, an optimized design for the water distribution network is proposed. This design considers factors such as hydraulic efficiency, pressure management, and resilience to minimize water losses and ensure consistent water supply to consumers. Furthermore, the study incorporates considerations for future growth and development in the Jabalpur Cantonment Board Area. By employing sustainable design practices and incorporating modern technologies, the proposed water distribution network aims to meet the present and future water needs of the community while minimizing environmental impact. By implementing the proposed design improvements, the Jabalpur Cantonment Board can achieve a more sustainable, efficient, and reliable water supply network, ultimately improving the quality of life for its residents.

Biodiesel supply chain network design: a comprehensive review with qualitative and quantitative insights.

The global community is actively pursuing alternative energy sources to mitigate environmental concerns and decrease dependence on fossil fuels. Biodiesel, recognized as a clean and eco-friendly fuel with advantages over petroleum-based alternatives, has been identified as a viable substitute. However, its commercialization encounters challenges due to costly production processes. Establishing a more efficient supply chain for mass production and distribution could surmount these obstacles, rendering biodiesel a cost-effective solution. Despite numerous review articles across various renewable energy supply chain domains, there remains a gap in the literature specifically addressing the biodiesel supply chain network design. This research entails a comprehensive systematic literature review (SLR) focusing on the design of biodiesel supply chain networks. The primary objective is to formulate an economically, environmentally, and socially optimized supply chain framework. The review also seeks to offer a holistic overview of pertinent technical terms and key activities involved in these supply chains. Through this SLR, a thorough examination and synthesis of existing literature will yield valuable insights into the design and optimization of biodiesel supply chains. Additionally, it will identify critical research gaps in the field, proposing the exploration of fourth-generation feedstocks, integration of multi-channel chains, and the incorporation of sustainability and resilience aspects into the supply chain network design. These proposed areas aim to address existing knowledge gaps and enhance the overall effectiveness of biodiesel supply chain networks.

Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization

Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. Traditional optimization methods often struggle to adapt to the complex and dynamic nature of these systems. This paper presents a novel blockchain-based zero-trust supply chain security framework integrated with deep reinforcement learning (SAC-rainbow) to address these challenges. The SAC-rainbow framework leverages the Soft Actor–Critic (SAC) algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC-rainbow algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The SAC-rainbow framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles. This research paves the way for developing secure, transparent, and efficient supply chain management systems in the face of growing complexity and security risks.

A novel optimal identification of various solar PV cell parameters by using MRDT controller

At present, Renewable Energy Sources (RES) utilization keeps on increasing because of their merits are more availability in the atmosphere, easy energy harvesting, less maintenance expenses, plus more reliability. Here, the solar power generation systems are utilized for supplying the energy to the local consumers. The accurate, and efficient solar power supply to the customers is a very important factor to meet the peak load demand. The accurate power generation of the sunlight system completely depends on its accurate parameters extraction. In this work, a Modified Rao-based Dichotomy Technique (MRAODT) is introduced to identify the actual parameters of the different PV cells which are PWP 201 polycrystalline, plus RTC France. The proposed MRAODT method is compared with the other existing algorithms which are the teaching and learning algorithm, African vultures, plus tuna intelligence algorithm. Finally, from the simulation results, the MRAODT gives superior performance when associated with the other controllers in terms of parameters extraction time, accuracy in the PV cells parameters identification, plus convergence time of the algorithm.