Operating Performance Assessment Research Articles

Assessment of operational performance and service quality in inland waterway transport: A case study of Warri Southwest, Nigeria

Purpose: This study aims to evaluate the operational performance and service quality of inland waterway transport in Warri Southwest, Nigeria, by examining the factors that influence them. Methodology: The study employs a quantitative research approach, using a structured questionnaire to collect data from 180 respondents. The data is analyzed using regression analysis and a structural equation model (SEM) with the help of PLS Software Version 13.0. Results: The findings indicate that cargo clearance, infrastructure quality, and storage facilities significantly influence the performance of agent operation in inland waterway transport (p=0.000). Staff performance also directly impacts the provision of logistical services, which affects customer service. However, jetty operations do not promote operational performance (p=0.075). Additionally, staff performance does not significantly influence the association between jetty operation and performance and the relationship between cargo clearance and infrastructure level (p=0.136, 0.388). Theoretical contribution: This study contributes to the literature on inland waterway transport by providing empirical evidence on the factors influencing operational performance and service quality. The findings can help inform the development of policies and strategies to improve the efficiency and effectiveness of inland waterway transport in Nigeria and other similar contexts. Practical implications: The study's recommendations include improving the quality of infrastructure, cargo clearance processes, and storage facilities to enhance operational performance. Additionally, investing in staff training and development can improve the provision of logistical services and, in turn, customer service. The study also suggests that the government and relevant stakeholders should prioritize the development of inland waterway transport to unlock its potential for socio-economic development, particularly in rural and hard-to-reach areas.

A spatiotemporal synergetic operating performance assessment based on reconstructed correlation matrix and dissimilarity analytics for plant-wide hot strip mill process

Operating performance assessment (OPA) of process industries plays a significant role in improving the product quality and pursuing the best economic benefits. With the continuous improvement of industrial intelligence, massive data has been collected. Variable time-delay issue is brought by the different spatial and temporal distributions of production units, which affects the actual causal relationship of collected data and assessment accuracy. Therefore, a spatiotemporal synergetic operating performance assessment (SSOPA) method is proposed in this paper. First, a reconstructed correlation matrix is carefully crafted in both time and space, leveraging the intricate multi-correlated spatiotemporal-delay (MCSD) parameters. To precisely quantify the multi-correlated relationships, a mixed gray relation analysis (MGRA) is employed. Subsequently, a predatory search-based genetic algorithm (PSGA) is utilized to meticulously search for the optimal MCSD parameters. Once these parameters are determined, an OPA model is formulated for each subsystem, drawing upon aligned process data and a distributed dissimilarity analysis (DISSIM). Finally, based on the subsystem-level OPA, a comprehensive assessment of the global operating performance level is conducted. The feasibility and effectiveness of the proposed method are verified by the plant-wide hot strip mill process (PHSMP).

Sustainability and operational performance assessment of supermarket air-conditioning architectures using secondary fluids and slurries

Secondary loop architectures and phase change material (PCM) slurries could reduce the environmental impact of refrigeration and air-conditioning systems by lowering significantly the amount of primary refrigerants. However, there is a barrier to their industrial development due to the lack of studies on their behaviour in operational scenarios. The present work analyses the behaviour of different secondary loop systems in an industrial context, here the air conditioning of a supermarket in France. For this purpose, a generic approach, developed in previous work, is proposed to assess their potential of adoption. This modelling approach is able to describe the sustainability and operational performance (energy, environmental, economic, social) of refrigeration systems. After validation of the model on real architecture and components, five standard or new architectures were tested: centralized direct expansion system; secondary loop system with ethylene glycol water; secondary loop system with ice slurries, TBPB hydrate slurries or CO2 hydrate slurries. The main results of this study show that new secondary systems using hydrate slurries have better cooling energy performance than direct expansion or classical brine / ice slurry secondary systems. Moreover, even including pumping power, the life cycle climate performance of secondary loop systems is much better than that of direct expansion systems. However, the total cost of ownership of direct expansion systems is lower than that of secondary systems, but with little differences for smaller pipes. Finally, trade-offs between several performances can be proposed. For example, some architectures with suitable pipe diameters could meet TCO/LCCP-based and CAPEX/pumping-based trade-offs.

Adaptation Entropy Regularization Actor–Critic for Process Operation Performance Assessment

Adaptation Entropy Regularization Actor–Critic for Process Operation Performance Assessment

Performance in Irish Selection and Future Performance in Surgical Training

Selection processes for surgical training should aim to identify candidates who will become competent independent practitioners and should aspire to high standards of reliability and validity. To determine the association between measured candidate factors at the time of an Irish selection and assessment outcomes in surgical training, examined via rate of progression to Higher Specialist Training (HST), attrition rates, and performance as assessed through a multimodal framework of workplace-based and simulation-based assessments. This retrospective observational cohort study included data from all successful applicants to the Royal College of Surgeons in Ireland (RCSI) national Core Surgical Training (CST) program. Participants included all trainees recruited to dedicated postgraduate surgical training from 2016 to 2020. These data were analyzed from July 11, 2016, through July 10, 2022. Selection decisions were based on a composite score that was derived from technical aptitude assessments, undergraduate academic performance, and a 4-station multiple mini-interview. Assessment data, attrition rates, and rates of progression to HST were recorded for each trainee. CST performance was assessed using workplace-based and simulation-based technical and nontechnical skill assessments. Potential associations between selection and assessment measures were explored using Pearson correlation, logistic regression, and multiple linear-regression analyses. Data were available for 303 trainees. Composite scores were positively associated with progression to HST (odds ratio [OR], 1.09; 95% CI, 1.05-1.13). There was a weak positive correlation, ranging from 0.23 to 0.34, between scores and performance across all CST assessments. Multivariable linear regression analysis showed technical aptitude scores at application were associated with future operative performance assessment scores, both in the workplace (β = 0.31; 95% CI, 0.14-0.48) and simulated environments (β = 0.57; 95% CI, 0.33-0.81). There was evidence that the interpersonal skills interview station was associated with future performance in simulated communication skill assessments (β = 0.55; 95% CI, 0.22-0.87). In this study, performance at the time of Irish national selection, measured across technical and nontechnical domains in a multimodal fashion, was associated with future performance in the workplace and in simulated environments. Future studies will be required to explore the consequential validity of selection, including potential unintended effects of selection and ranking on candidate performance.

Operating Performance Assessment of Complex Nonlinear Industrial Process Based on Kernel Locally Linear Embedding PLS

Operating Performance Assessment of Complex Nonlinear Industrial Process Based on Kernel Locally Linear Embedding PLS

Process Operating Performance Assessment for Plant-Wide Froth Flotation via Distributed Multigraph Deep Embedding Graph Clustering Network

Process Operating Performance Assessment for Plant-Wide Froth Flotation via Distributed Multigraph Deep Embedding Graph Clustering Network

Operating performance assessment based on stacked performance‐relevant enhanced denoising auto‐encoder for industrial processes

AbstractAs an effective way to ensure the economic benefits of enterprises, process operating performance assessment has attracted more and more attention from industry and academia in recent years. In this paper, a stacked performance‐relevant enhanced denoising autoencoder (SPEDAE) network is designed for the operating performance assessment of industrial processes. Compared to the original denoising auto‐encoder (DAE), each performance‐relevant enhanced denoising auto‐encoder (PEDAE) not only reconstructs the input features in the output layer, but also strives to reconstruct the original input data and the performance grade labels simultaneously. Then the SPEDAE is formed by stacking multiple PEDAEs layer by layer. Through this improved training strategy, SPEDAE can avoid accumulated information loss during the deep feature extraction process, improve the robustness of the network, and extract features closely related to the operating performance, thereby better completing the assessment task. The effectiveness of the proposed assessment method is validated on the case of gold cyanide leaching process. Compared with several methods, the proposed SPEDAE has the highest accuracy and reaches 99.85%, which demonstrates its superiority in operating performance assessment.

Evaluation of the performance of the compost plant for optimal operational evaluation.

Composting is a municipal solid waste management strategy that can reduce municipal waste mixtures to diminish the volume and weight of disposable materials, decrease leachate emissions, recycle resources, and reduce the costs required for waste disposal. Today, the wave of consumerism in the current world and the advancement of technology has led to producing diversified waste around the globe, so the growth rate of all countries, including Iran, is increasing dramatically. This research was studied from a practical point of view for the first time in Karaj. We used a method based on a meta-heuristic algorithm to analyze the data. The results of these studies show that units that are part of large industries have a greater environmental approach than other smaller food industries because most of the smaller food industries have environmental experts or other findings; an environmental group is considered costly and troublesome. Therefore, the present research focuses on the thorough assessment of a compost plant's operational performance to achieve maximum efficiency and effectiveness. Technical, environmental, and economic viewpoints are all included in the assessment's multidimensional approach. Moreover, this case study of the Karaj compost factor summarizes the effectiveness of the method based on a meta-heuristic algorithm adopted for waste management. Therefore, it can be said that the root of the existing problems concerning the proper management of food industry waste in Karaj is due to the lack of environmental experts in the industry.

Data anomaly detection with automatic feature selection and deep learning

As increasing sensor-based structural health monitoring (SHM) systems are implemented on civil infrastructures, sensor data reliability plays a crucial role in the assessment of operational performance of bridges. Sensors are heavily exposed to harsh environmental conditions during their operations and inevitably lead to possible unstable performance or failure. Thus, to accurately identify faulty sensors is a prerequisite to processing and analyzing the collected data for assessment purpose. Recently, researchers adopted the convolutional neural network (CNN) approach to identify faulty sensors, focusing on image features. Such approach may overlook some important detailed signal features and the time series approach may still be needed. However, algorithms based on time series tend to be time consuming because of the lengthy and high dimensional dataset. This may be effectively resolved using an automatic feature selection technique, namely Tsfresh, as proposed in this paper to select highly relevant signal features based on statistical tests of significance. A deep learning technique based on fully convolutional network (FCN) can then be efficiently employed for anomaly classification. The algorithm is validated using a dataset collected from a real cable-stayed bridge and results show that the proposed method significantly reduces the training time for the neural network, albeit with high classification accuracy.

Process operation performance assessment of electro‐fused magnesium furnace based on deep auto‐encoder transfer generative adversarial network

AbstractThe process operation performance assessment (POPA) of the smelting process of electro‐fused magnesium furnaces (EFMFs) plays a very important role for improving production quality and pursuing the highest economic benefit. Most of the existing methods are based on the assumption of abundant labelled training samples. However, the lack of samples for the POPA is a challenging problem in the smelting process of EFMFs. Traditional methods for POPA make it difficult to obtain satisfactory results under small samples. A new deep auto‐encoder transfer generative adversarial network (DAETGAN) based on source domain data is proposed to improve the performance of POPA with small samples in EFMF smelting process. Firstly, a general transfer framework is proposed, in which the data of source domain is used as input to generate adversarial network (GAN), and a large number of images, sound, and current samples are generated with the same distribution as the data of target domain so as to improve the transfer learning effect. Secondly, a POPA model is proposed with the multi‐source heterogeneous information generated by DAETGAN and the original multi‐source heterogeneous information in target domain. Finally, it is verified by experiments that the DAETGAN model can generate data with the same distribution as the original data, and the accuracy of the assessment of operational performance is effectively improved.

Construction Digital Technology Assimilation and Absorption Capability Using Measurement Invariance of Composite Modeling

Digital technology is evolving, and construction companies now have various choices of these technologies to implement into daily business. This has, however, resulted in a growing argument on how digital technologies can be accepted in various contexts. However, most arguments are based on technology acceptance models with the core concept of individual behaviors, which ignores the influence of other stakeholders and organizational characteristics. This paper aims to present a novel model representing the successful practice of the assimilation process for digital construction technologies that should be considered before any technology acceptance analysis. The model was tested via empirical investigation with 372 practitioners from Australia involved in building and infrastructure construction projects. Structural equation modeling, measurement invariance of composite models, and some semistructured interviews were used to test and verify the relationships between key variables of the model. The results suggest that the capability of assimilation can be enhanced by improving innovativeness and the relevant operational processes, with a t-value of >6.27. The findings show that a project’s likelihood of successful technology assimilation can also be increased if vendors are supportive and the company improves its operating performance assessment and absorption capacity. The findings clarify theoretical and practical contributions for a better understanding of critical factors of assimilation and provide a tool to potential company users for predicting the success of technology assimilation.

Assessing operative competence in core surgical training: A reliability analysis

BackgroundThis study quantifies the number of observations required to reliably assess the operative competence of Core Surgical Trainees (CSTs) in Ireland, using the Supervised Structured Assessment of Operative Performance (SSAOP) tool. MethodsSSAOPs (April 2016–February 2021) were analysed across a mix of undifferentiated procedures, as well as for three commonly performed general surgery procedures in CST: appendicectomy, abdominal wall hernia repair, and skin/subcutaneous lesion excision. Generalizability and Decision studies determined the number of observations required to achieve dependability indices ≥0.8, appropriate for use in high-stakes assessment. ResultsA total of 2,294 SSAOPs were analysed. Four assessors, each observing 10 cases, can generate scores sufficiently reliable for use in high-stakes assessments. Focusing on a selection of core procedures yields more favourable reliability indices. ConclusionTrainers should conduct repeated assessments across a smaller number of procedures to improve reliability. Programs should increase the assessor mix to yield sufficient dependability indices for high-stakes assessment.

A Scoping Review of Simulation-Based Training Paradigms for Carotid Artery Endarterectomy and Carotid Artery Stenting

Surgical simulation has come to the forefront to enhance the training of residents. The aim of our scoping review is to analyze the available simulation-based carotid revascularization techniques, including carotid endarterectomy (CEA) and carotid artery stenting (CAS) and suggest critical steps for evaluating competency in a standardized fashion. A scoping review of all reports on simulation-based carotid revascularization techniques including CEA and CAS was performed in PubMed/MEDLINE, Scopus, Embase, Cochrane, Science Citation Index Expanded, Emerging Sources Citation Index, and Epistemonikos databases. Data were collected according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. The English language literature was searched from January 1, 2000 to January 9, 2022. The outcomes evaluated included measures of assessment of operator performance. Five CEA and 11 CAS manuscripts were included in this review. The methods of assessments employed by these studies to judge performance were comparable. The 5 CEA studies sought to validate and demonstrate improved performance with training or distinguish surgeons by their experience level, either through assessing operative performance or end-product results. The 11 CAS studies used 1 of 2 types of commercial simulators and focused on determining the efficacy of simulators as teaching tools. By examining the steps of the procedure associated with preventable perioperative complications, it provides a reasonable framework for determining which elements of the procedure should be emphasized most. Furthermore, using potential errors as a basis for assessment of competency could reliably distinguish operators based on level of experience. Competency-based simulation training is becoming more relevant as our surgical training paradigm shifts with the increased scrutiny within training programs of work-hour regulations and the need to develop a curriculum to assess our trainees' ability to perform specific operations competently during their stipulated training period. Our review has given us an insight into the current efforts in this space regarding 2 specific procedures that are key for all vascular surgeons to master. Although many competency-based modules are available, there is a lack of standardization in the grading/rating system of what surgeons consider vital steps of each procedure to assess these simulation-based modules. Therefore, the next steps of curriculum development should be based on standardization efforts for the different protocols available.

Recoupled: A simulation tool for renewable energy communities coupling electric and thermal energies

Renewable energy communities (RECs) are legal entities where citizens, small-to-medium en- terprises (SMEs) and local authorities join to manage cooperatively energy from renewable sources. Since the regulation requires to evaluate energy fluxes on the hour base, the operative control and performance assessment of these new energy hubs become complex and require the handling of data such as production from renewable energy sources (RES) and end user con- sumption, that are intrinsically affected by uncertainties. In this contribution, an optimization tool for the operational management of a REC is proposed. RECs can contain renewable energy technologies (photovoltaic or solar thermal panels, biofuel burners), electric, heating and cooling end users and coupling components (e.g., heat pumps). The tool can be used at the planning level to compare different REC configurations based on their performances, assuming optimal man- agement of the available technologies. In this paper, the tool is tested in the simulation of three case studies of collective self-consumption (that in Italy is a REC where all end users are in the same building), located at different latitudes of the Italian country.

Process Operation Performance Assessment Based on Multisource Heterogeneous Information With Semisupervised Transfer Generative Adversarial Network for Electrofused Magnesium Furnace

The process operation performance assessment in the smelting process of electro-fused magnesium furnace (EFMF) plays a very important role in improving production efficiency and economic benefits. Most of the existing methods mainly rely on a large number of labeled data, which is difficult to satisfy in practical application. To solve the problem of a small number of labeled samples, a semi-supervised transfer generative adversarial network (SSTGAN) based on source domain (SD) data is proposed to assess the operation performance of EFMF. The core idea is to use the labeled data of SD to train a classifier and predict the unlabeled data of the target domain (TD), and use the GAN to make the multi-source heterogeneous information comprehensive features of the TD and SD conform to the similar distribution. The experiment shows that the method can obtain satisfactory assessment results than the compared method.

Deep Learning for Operating Performance Assessment of Industrial Processes with Layer Attention-Based Stacked Performance-Relevant Denoising Auto-Encoders.

The operating performance of a process may degenerate due to process interferences and operation errors, which cancel the benefits of technology design and economic production. Traditional operating performance assessment methods are either lack of real-time due to the post-analysis or difficult to distinguish performance grades for process data with weak differences and strong noise interferences based on shallow learning structures. In this paper, a new layer attention-based stacked performance-relevant denoising auto-encoder (LA-SPDAE) is proposed for the operating performance assessment of industrial processes. It overcomes the defect that the original SDAE ignores task-relevant information in training and only uses the feature of the last hidden layer to complete special tasks. In this study, the original SDAE is improved by optimizing the cross-entropy loss of the performance grade labels in the layer-wise pretraining, which is named stacked performance-relevant denoising auto-encoder (SPDAE), and the performance-relevant features can be extracted under supervision. Moreover, for making good use of performance-relevant features of each layer, they are fused by adaptive weights based on the layer attention mechanism. In the case study of cyanide leaching, the assessment accuracy of the proposed LA-SPDAE model is up to 99.85% under the corrupted proportion of 20%, and the advantage is still maintained as the proportion increases to 80%, which demonstrates the superiority of LA-SPDAE compared with conventional deep neural networks and shallow learning structures.

Operating performance assessment method for industrial process with slowness principle-based LSTM network

Timely and accurate performance assessment and non-optimal regulation of industrial processes can effectively guarantee product quality. Most industrial processes are highly nonlinear and dynamic, so long short-term memory (LSTM) network is suitable for industrial performance assessment. However, in the network learning, the typical LSTM network focuses on the representation learning of input variables, lacks the representation of comprehensive economic indexes (CEI), and cannot selectively learn essential features, which increases the computational burden and easily mixes redundant information. Thus, a supervised slow feature analysis (SFA)-based LSTM (SSFALSTM) network is proposed for industrial operating performance assessment. By utilizing CEI information and SFA constraints, the network is guided to simultaneously learn features related to CEI and slow-changing features that reflect the inherent dynamics of the process. Further, cascade performance recognition model to construct the complete performance assessment framework. For the non-optimal performance, a reconstruction-based contribution plot method is proposed to identify the main cause variables and guide adjustment operations. Finally, the effectiveness of the proposed method is validated on the dense medium coal preparation process.

ACT-R based human digital twin to enhance operators' performance in process industries.

To ensure safe and efficient operation, operators in process industries have to make timely decisions based on time-varying information. A holistic assessment of operators' performance is, therefore, challenging. Current approaches to operator performance assessment are subjective and ignore operators' cognitive behavior. In addition, these cannot be used to predict operators' expected responses during novel situations that may arise during plant operations. The present study seeks to develop a human digital twin (HDT) that can simulate a control room operator's behavior, even during various abnormal situations. The HDT has been developed using the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture. It mimics a human operator as they monitor the process and intervene during abnormal situations. We conducted 426 trials to test the HDT's ability to handle disturbance rejection tasks. In these simulations, we varied the reward and penalty parameters to provide feedback to the HDT. We validated the HDT using the eye gaze behavior of 10 human subjects who completed 110 similar disturbance rejection tasks as that of the HDT. The results indicate that the HDT exhibits similar gaze behaviors as the human subjects, even when dealing with abnormal situations. These indicate that the HDT's cognitive capabilities are comparable to those of human operators. As possible applications, the proposed HDT can be used to generate a large database of human behavior during abnormalities which can then be used to spot and rectify flaws in novice operator's mental models. Additionally, the HDT can also enhance operators' decision-making during real-time operation.

Operating Performance Assessment Based on Semi-Supervised Cluster Generative Adversarial Networks for Gold Flotation Process

Operating performance assessment of gold flotation process plays an important role in improving the metallurgical performances and pursues the best comprehensive economic benefits. The appearance of froth is a good indicator of flotation performance; however, labeling the data is costly. To assess the operational performance of flotation process with a small amount of labeled data, this study proposes a semi-supervised cluster generative adversarial network (SSClusterGAN). First, latent variables are sampled from one-hot encoded variables and continuous normal distribution variables. While projecting all data into the latent variable space, the specific types of clustering in the latent space is realized by controlling the type of distribution of the learned latent codes, so that the training objectives of labeled data and unlabeled data are consistent. Then, by adapting the generative adversarial networks (GANs) framework, a pair of stacked discriminators are used to learn the conditional distribution of attributes for labeled and unlabeled data, respectively. In addition, using semi-supervised learning (SSL) as the basic learning technology, the high-confidence pseudo-labels obtained by training with unlabeled data are combined with the labeled training data to increase the training samples and effectively improve the accuracy of the model. Finally, the method was applied to the gold flotation process. The experimental results show that the method effectively utilizes unlabeled samples to expand the labeled dataset, combines the advantages of SSL and GAN, obtains accurate assessment results, and effectively improves the comprehensive economic benefits of the gold flotation process.