Plant Assets Research Articles

Accounting Treatment of Coffee as Bearer Plant Asset at Perumda Perkebunan Kahyangan Jember

This study explores the accounting treatment of coffee plant as a bearer plant asset at Perumda Perkebunan Kahyangan Jember, a local state-owned enterprise in East Java Province Indonesia. As the agricultural sector plays a crucial role in Indonesia's economy, the proper classification, recognition, measurement, and disclosures of coffee plant as bearer plants are critical for financial transparency and effective management. This study examines how relevant Indonesian Financial Accounting Standards (i.e. PSAK 16 Revision 2011 on Fixed Assets, PSAK 69 on Agriculture, PSAK 14 on Inventory, and PSAK 48 on Impairment of Assets) and International Financial Reporting Standard (i.e. IAS 41) applied in the financial reporting of coffee as a bearer plant at Perumda Perkebunan Kahyangan Jember. Using a qualitative research method with the case study approach, this study provides empirical evidence on how the implementation of such accounting standards, and the obstacle faced by the company presented based on the perspective of recognition, measurement, and disclosure relevant to coffee plant.

Asset management knowledge graph for production plants in process industry

Abstract The introduction and adoption of various semantic models for describing assets within a plant have paved the way for new opportunities. This contribution centers on the establishment and continuous maintenance of a plant asset management system based on knowledge graphs, utilizing the information contained in various semantic models. Additionally, it highlights how the graph can be leveraged in the creation and configuration of AI-based anomaly detection models, aimed at the monitoring of measurement values in a process plant. The concept paves the way for the wide deployment of AI models in monitoring applications in process industry.

Hybrid power plants: An effective way of decreasing loss-of-load expectation

Diversifying variable renewable resources by combining wind, solar photovoltaic, and battery assets in a hybrid power plant can increase renewable energy usage efficiency and improve system flexibility, particularly in distributed energy systems. However, the resilience impact of these systems, particularly outage mitigation, can be difficult to quantify due to uncertainty in resource, energy demand, and outage occurrence. This study outlines a framework to quantify the incremental benefit of hybrid power plant assets for reducing loss-of-load expectation during random outage events. Hybrid power plant performance during outages (considering varying duration and severity) is simulated using a Monte Carlo methodology to reflect uncertainty associated with renewable resource, load demand, and outage timing. Results demonstrate the additional incremental value from increasingly hybrid designs, in which relative capacities of wind, solar photovoltaic, and storage assets contribute to lower loss-of-load expectation than the constituent technologies would alone. The value of added wind or solar capacity increases as the plant composition approaches an equal split. The value of added battery capacity depends on the outage duration and severity, but the first 50 MWh of added storage capacity is the most valuable for reducing the loss-of-load expectation for all plant designs.

Planning for wastewater infrastructure adaptation under deep uncertainty

Infrastructure in low-lying coastal areas faces challenges from climate change, sea level rise, and the impact of compound hazards. Dynamic adaptive pathways planning (DAPP) is increasingly being applied as a way of planning under deep uncertainty. Stress testing for robustness is an integral part of DAPP which provides decision-makers with confidence. We outline a seven-step approach—combining scoping workshops, systems mapping, DAPP, exploratory modelling, robust decision-making, real options analysis and validation workshops—to support decision-making for infrastructure in low-lying coastal areas. We apply the seven steps to two wastewater treatment plant (WWTP) case studies in New Zealand to quantify indicators, signals, triggers and adaptation thresholds within DAPP plans and to identify adaptation pathways that are robust against future uncertainty. Case study one focuses on the implementation of an existing DAPP at Helensville WWTP. Our modelling enabled the challenge of quantifying indicators for adaptation thresholds and triggers to be overcome. We show that an adaptation threshold occurs at 31 cm of RSLR, the trigger point is sufficient lead time to enable relocation, and the indicator is the rate of observed RSLR. Case study one demonstrates in a quantitative way how an existing DAPP can be functionally implemented by a water management agency. Modelling for case study two, the Seaview WWTP, showed that 26 cm and 56 cm of RSLR are key thresholds. Nuisance flooding may occur after 26 cm of RSLR, which could happen as early as 2040 under a high emissions scenario. Inundation of plant assets may occur after 56 cm of RSLR, which could occur as early as 2060. Modelling showed that implementing changes to plant layout would allow the plant to remain on site for its design life (until 2080). Five adaptation archetypes were developed—sequences of adaptive actions that achieve the performance objective of continuing levels of service and avoid inundation of WWTPs. The seven-step approach is a way to stress-test a DAPP, to quantify signals, triggers and adaptation thresholds and to simulate implementation of a DAPP under a range of scenarios. This can facilitate more robust decision-making for wastewater infrastructure assets under future uncertainty.

The Moderating Effect of Sustainability Reporting on the Influence of Tax Avoidance on Firm Value

The purpose of this research is to obtain empirical evidence supporting the hypothesis that sustainability reports can reduce shareholders' negative reactions to tax avoidance. The selected research sample consists of non-financial companies listed on the Indonesia Stock Exchange over a period of five years (2018-2022). The research data were tested using panel regression methods. The results of the test on 1.690 observation data indicate that only 40% of the samples engage in sustainability reporting. This suggests that a considerable number of publicly listed companies in Indonesia do not engage in sustainability reporting. The results of the analysis indicate that tax avoidance does not have a significant impact on the company's value. Sustainability reporting as a moderating variable has a positive effect on the relationship between tax avoidance and the company's value. Several control variables have a significant impact on firm value, namely leverage, profitability, and plant assets. This study contributes to the analysis of the impact of tax avoidance on the value of companies in Indonesia, moderated by sustainability reporting.

Identification of surface defects on solar PV panels and wind turbine blades using attention based deep learning model

The global generation of renewable energy has rapidly increased, primarily due to the installation of large-scale renewable energy power plants. However, monitoring renewable energy assets in these large plants remains challenging due to environmental factors that could result in reduced power generation, malfunctioning, and degradation of asset life. Therefore, the detection of surface defects on renewable energy assets is crucial for maintaining the performance and efficiency of these plants. This paper proposes an innovative detection framework to achieve an economical surface monitoring system for renewable energy assets. High-resolution images of the assets are captured regularly and inspected to identify surface or structural damages on solar panels and wind turbine blades. Vision transformer (ViT), one of the latest attention-based deep learning (DL) models in computer vision, is proposed in this work to classify surface defects. The ViT model outperforms other DL models, including MobileNet, VGG16, Xception, EfficientNetB7, and ResNet50, achieving high accuracy scores above 97% for both wind and solar plant assets. From the results, our proposed model demonstrates its potential for monitoring and detecting damages in renewable energy assets for efficient and reliable operation of renewable power plants.

Exploration of Ascochyta rabiei diversity through various molecular tools and control of its pathogenic strain via green synthesized zinc oxide nanoparticles

Fungus is the most important disease-causing biotic factor on earth. The study of their genetic diversity is very important for their management and control. For studying Ascochyta rabiei on chickpea blight chickpea samples caused by Ascochyta rabiei were collected and disease-causing organisms were isolated from infected plant parts and their ITS region was sequenced. BLAST analysis revealed that A. rabiei is the causal pathogen of this blight disease. Phylogenetic analysis suggested great diversity among these isolates of A. rabiei. The results confirm that A. rabiei strains collected from Bannu and Lakki Marwat have genetic similarities but are different from other isolated strains of the same species. Similarly, studied strains from Kamber, Shadat Kot, and Larkana. Khairpur and Bhakkar were also similar. However, strains from Khushab and Mianwali showed genetic differences from each other. The current research also reports the plant-mediated synthesis of ZnO NPs via the plant extract of Thevetia peruviana. The ZnO NPs were characterized through UV, XRD, FTIR, FE-SEM, EDX, and the size of the nanoparticle of 28 nm was calculated. The prepared nanoparticle was used for the antifungal activity against the most severe strain of A. rabiei. The Biosynthesis of ZnO NPs by plant assets is an eco-friendly, consistent process and appropriate for extensive production.

Consider Effective Useful Life to Improve Asset Replacement Planning

A new AWWA tool analyzes effective useful life for water treatment plant and pumping station assets, allowing utilities to make targeted, data‐driven, and timely rehabilitation or replacement decisions.

Insights into material recovery, revenue, and global warming potential of India’s end-of-life photovoltaic installations reveals the urgent need for blockchain-based solar passports

Growing photovoltaic (PV) panel waste is creating numerous environmental problems and an unfamiliar opportunity to add value and explore new business options in line with circular economy practices. As an immediate action, countries struggling with massive amounts of PV waste started exploring end-of-life management options, inventory analysis for PV waste, regulatory measures to handle, and asset monetization solutions for commissioning/decommissioning solar PV systems. Nevertheless, these actions have not been decisively implemented in many countries and have thus remained unfulfilled policy aspirations. In the context of India, it is worth noting that the country has established a set of industrial solid waste rules that are fairly comprehensive; however, they do not encompass the issue of PV waste. Hence, this study aims to understand India’s current stand on PV waste and provide a holistic solution for effective PV waste management in the long run. For this, a comprehensive look into India’s grid-connected PV plants was done to provide insights on the PV waste inventory potential, material recovery potential under various recycling approaches, possible revenues earned by selling recovered resources in the marketplace, and global warming potential for treated PV waste. Later, a survey-based discussion was done highlighting the reasons for uncertainties and potential pitfalls with installed solar inventory data considering ground reality situations. Lastly, based on the observed results and associated uncertainties, this study proposes the blockchain-based solar passports framework with a case illustrating the implementation as an urgent need in India to effectively monetize solar PV plant assets.

Olah Produk Tanaman Pisang Dalam Peningkatan Ekonomi Masyarakat Desa Karanglo Kidul Kecamatan Jambon Kabupaten Ponorogo

Based on the results of the team's survey in Dukuh Tengah, there are potential assets that can be developed, including banana plants. Plants that do not have this season grow a lot in the environment of Dukuh Tengah, therefore in this discussion took the theme with the title "Training on processing food products to increase the Selling Value of banana plants in Dukuh Tengah village of Karanglo Kidul". The process of processing bananas into steamed sponges is very simple, simply by using existing tools and ingredients that are easy to obtain. So we use the demonstration method to carry out training on processing banana steamed sponges in the middle village of Karanglo Kidul Village, so that the people of the middle village can easily understand and practice directly processing bananas into steamed sponges in their homes. By making banana plant assets that are processed into banana steamed sponges that have more selling price value and of course this activity can improve the quality of human resources of the dukuh tengah community who are innovative and creative. Hopefully the team hopes that later, the results of the banana steamed sponge processing training can increase economic results and can increase public insight and knowledge about banana processing innovations so that they have more selling value.

Asset structure, leverage, and value of listed firms: Evidence from Kenya

Firm value shows the performance of a firm while reflecting the present value of the firm’s future cashflows, hence affecting investment decisions. Therefore, this paper explores the relationship between asset structure, leverage, and firm value of 51 listed companies between 2010 and 2019 using secondary data collected from audited financial statements. The study applies panel data regression models and the causal-comparative research design. The quantitative data are analyzed using multiple regression. The result shows that plant, equipment, property, current, and financial assets influence the firm value positively. Nonetheless, the quotient of current to total assets was reported to yield the highest beta coefficient, implying that significant firm value creation is realized for every additional current asset held, weighed against the quotient of additional equipment, property, and plant to the value of total assets. Leverage had an insignificant influence on the value of firms, implying that no maximization of value is attainable in manufacturing firms through the astute use of borrowed funds. The study recommends that finance pundits consider firms’ asset structure and the use of borrowed funds when formulating financial and investment policies. The study enriches the scholarly world by developing a model for establishing the value of listed firms.

Bayesian Network–Based Fault Diagnostic System for Nuclear Power Plant Assets

Future advances in nuclear power technologies call for enhanced operator advice and autonomous control capabilities that can leverage simpler designs and increased safety features to reduce reliance on human labor. One of the first tasks in the development of such capabilities is the formulation of symptom-based conditional failure probabilities for the plant structures, systems, and components (SSCs) of interest. The primary goal is to aid plant personnel in (1) deducing the probabilistic performance status of the monitored SSCs and (2) detecting impending faults/failures. The task of estimating conditional failure probability is a bidirectional inference problem, and a logical approach is to use the Bayesian network (BN) method. As a knowledge-based explainable artificial intelligence tool and a probabilistic graphical model, BN offers reasoning capability under uncertainty, graphical representation emulating physical behavior of the target SSC, and interpretability of the model structure and results. This paper provides a systematic overview of the BN technique and the software tools for implementing BN models, along with the associated knowledge representation and reasoning paradigm. Both operational data and expert judgment can be readily incorporated into the knowledge base of a BN model. The challenges with data availability are highlighted, and the general approach to target SSC identification is presented. The focus is on failure-prone and risk-important balance of plant assets, especially for cases with strong operator involvement. Two example case studies on the failure of (1) a centrifugal pump and (2) an electric motor are conducted to demonstrate the usefulness and technical feasibility of the proposed BN-based fault diagnostic system using an expert system shell.

ISO 50001 Data Driven Methods for Energy Efficiency Analysis of Thermal Power Plants

This paper proposes an energy management system based on an Artificial Neural Network (ANN) to be integrated with the standard ISO 50001 and aims to describe the definition and the enhancement of the energy baselines by means of artificial intelligence techniques applied and tested on the real electrical absorption data of the auxiliary units of different thermal power plants in Italy. Power plant optimized operations are important both for cost and energy performance reasons with related effects on the environment in the next future energy transition scenario. The improvement of the energy baselines consists in determining more accurate consumption monitoring models that are able to track inefficiencies and absorption drifts through data analytics and Artificial Intelligence. Starting from the analysis of the energy vectors at the production site level, we performed a multi-scale analysis to define the consumption at macro areas level and finally find the most relevant consumption units within the plants. A comparison of different ANNs applied to several real power plant data was performed to model complex plant architecture and optimize energy savings with respect to pre-set thresholds according to the ISO 50001 standard procedure. The energy baselines are determined through the analysis of the data available in the power plants’ Distributed Control System (DCS), and we can identify the consumption derived from the unit’s proper operation. Based on the reported numerical simulations, improved baselines have been reached up to a 5% threshold for different plant sub-units, thus representing a relevant overall saving in terms of alert threshold definition and related control efficiency: a potential saving of about 140 MWh throughout the considered three-year dataset was obtained taking into account a cooling tower sub-unit, representing a considerable economic benefit. The results obtained highlight the neural technique efficiency in defining more accurate energy baselines and represents a valuable tool for large energy plant asset management to face relevant energy drifts observed in the last years of plant operation.

Importance of “SMART valve positioners” in testing of final control elements in safety instrumented systems application

AbstractMany industrial facilities, especially those in the chemical and oil and gas industries, involve inherent risk in operations due to the processing of materials that may be hazardous in nature. It is therefore necessary to precisely identify and analyze these hazards, operability issues, associated risks, and consequences. Safety instrumented systems (SIS) are often used to reduce the risk associated with processes or plants to an acceptable or tolerable level. The reliability of safety function(s) implemented through SIS is dependent on how reliable the sub‐elements of the safety function, that is, sensors, logic solvers, and final elements, are. The final elements of such a SIS usually include the emergency shutdown valves, depressurizing valves, emergency isolation valves, and critical on–off valves. Since these final elements remain continuously open or are sometimes closed during the normal operation of the plant, it becomes a challenge to operate the final elements when there is a demand on the SIS. During an emergency, if these final elements fail to operate, this contributes to the dangerous failure of the plant, which may result in explosion, fire, or catastrophic damage to plant assets, environment, and personnel. Hence, it is essential to test these final elements frequently while in operation, without disturbing the operation of the running plant, to ensure that these final elements are available to perform their intended functions when needed. Smart valve positioners are one of the options among various methods available to test the final elements without disturbing the process. This paper evaluates the application, functionality, diagnostics, and advantages of SMART positioners in the testing of final control elements in a SIS application.

Plant Asset in the Historical Spaces: Case of Diyarbakır, Turkey

Bu çalışmada, Diyarbakır ili Sur ilçesinin 12 cami ve 1 kilisesindeki bitki varlığının tespit edilmesi ve değerlendirilmesi amaçlanmıştır. Bu amaçla, çalışma alanlarındaki bitkiler teşhis edilmiş, dendrolojik özellikleri belirlenmiş, gövde çapları ölçülmüş ve tarihi nitelikli alanda kullanıma uygunlukları değerlendirilmiştir. Çalışmada toplam 199 ağaç ve çalı belirlenmiştir. Çalışma alanlarında en yaygın karşılaşılan türler doğu çınarı (Platanus orientalis L.) ve ak duttur (Morus alba L.). Ayrıca, tarihi mekânların iyileştirilmesi ve korunması açısından bu tür alanların çevresiyle birlikte korunması konusundaki çalışmaların yetersiz olduğu tespit edilmiştir. Bu anlamda, yanlış kullanılan türler bozkır bitki örtüsü içinde bulunan doğal bitkiler ile değiştirilebilir.

Community Perceptions of Agricultural Zakat in View of Sharia Economic Law (Leppangeng Village, Belawa District, Wajo Regency)

Agricultural zakat as stipulated in Law Number 23 of 2011 concerning Management of Zakat in Article 4 paragraph (2) states that zakat money includes agricultural and plant assets, forests, agricultural products and plants and forests belonging to individuals. muzaki. Or a business entity whose payment system is regulated according to Islamic law. However, there is still a lack of awareness of farmers about the obligation of zakat on agricultural products, as evidenced by the fact that most of them issue zakat on simple agricultural products (at first glance) without following the provisions regarding the amount of zakat. The research method used is a qualitative descriptive method that aims to determine people's perceptions of agricultural zakat and views of economic law. This research was conducted in Leppangeng Village, Kec. Belawa Regency. Wajo which takes place from 15 December 2021 to 15 February 2022. The results show that the public's perception of agricultural zakat is that 100% of the people who have been interviewed know the law of paying agricultural zakat is obligatory, 89% know the nisab and the percentage of agricultural zakat according to their respective perceptions and only 11% have the same perception as the author about the nisab and the percentage of agricultural zakat. The view of the community's Sharia Economic Law is that the agricultural zakat that has been issued by the community is in accordance with Islamic law in verses and hadiths.

The Effectiveness Analysis of National Social Forestry Activities in Enrekang Regency, South Sulawesi: A Case Study in Indonesia

This study aims to determine the effectiveness of the National Social Forestry Development Activities and to determine the level of community satisfaction as well as the benefits of these activities on increasing forest product in Enrekang Regency. The target of the research is the Social Forestry Business Group which receives the Nusantara Forestry Development activities in 2019 – 2021. This study uses interviews with respondents using questionnaires and direct observation to the location. The measurement of the effectiveness value uses the Guttman Scale, while the level of community satisfaction uses the Likers Scale. The level of achievement of effectiveness is measured using standards that are in accordance with the R&D reference of the Ministry of Home Affairs (1991). The results showed: (1) The value of the effectiveness of Kegiatan Perhutanan Sosial Nasionalin Enrekang Regency was 86.84% with an achievement level of "very effective", (2) The index value of the percentage level of community satisfaction with the implementation of Kegiatan Perhutanan Sosial Nasional in Enrekang Regency was 93,75 in the very satisfied category, (3) Activities that provide short-term benefits in the form of livestock yields and long-term benefits are Multi Purpose Tree Species (MPTS) plant assets and crop agroforestry that can be harvested in the next 5 years, which can increase yield production forest.

HT-TPP: A Hybrid Twin Architecture for Thermal Power Plant Collaborative Condition Monitoring

Thermal power plants, TPP, are one of the main players in the phosphoric acid and fertilizer production value chain. The control of power plant assets involves considerable complexity and is subject to several constraints, affecting the asset’s reliability and, most importantly, plant operators’ safety. The main focus of this paper is to investigate the potential of an agent-based digital twin architecture for collaborative prognostic of power plants. Based on the ISO 13374:2015 scheme for smart condition monitoring, the proposed architecture consists of a collaborative prognostics system governed by several smart DT agents connected to both physical and virtual environments. In order to apprehend the potential of the developed agent-based architecture, experiments on the architecture are conducted in a real industrial environment. We show throughout the paper that our proposed architecture is robust and reproduces TPP static and dynamic behavior and can contribute to the smart monitoring of the plant in case of critical conditions.

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power.
 This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

Plant conversions and abatement technologies cannot prevent stranding of power plant assets in 2\u2009\xb0C scenarios

Continued fossil fuel development puts existing assets at risk of exceeding the capacity compatible with limiting global warming below 2 °C. However, it has been argued that plant conversions and new abatement technologies may allow for a smoother transition. We quantify the impact of future technology availability on the need for fossil fuel power plants to be stranded, i.e. decommissioned or underused. Even with carbon capture and storage (CCS) and bioenergy widely deployed in the future, a total of 267 PWh electricity generation (ten times global electricity production in 2018) may still be stranded. Coal-to-gas conversions could prevent 10–30 PWh of stranded generation. CCS retrofits, combined with biomass co-firing, could prevent 33–68 PWh. In contrast, lack of deployment of CCS or bioenergy could increase stranding by 69 or 45 percent respectively. Stranding risks remain under optimistic technology assumptions and even more so if CCS and bioenergy are not deployed at scale.