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Microbiology of green fuels
\"A key priority in today's society is the implementation of a sustainable bio-based economy. For such a goal, the production of renewable bioproducts such as biofuels to replace fossil-derived compounds is crucial. In this context, the utilization of microorganisms for the production of biofuels from renewable resources is advantageous in terms of environmental sustainability and it is expected to play an important role in bioeconomy in the near future. In this sense, green fuel synthesis from agro-industrial organic wastes by microorganisms will boost circular economy. The success of the biotechnological biofuel production process requires, however, conversion microorganism capable of both efficiently assimilating the major derived carbon sources and diverting their metabolites towards the specific fuel. This book aims to show recent advances in the production of green fuels by means of microorganisms. Promising processes and microorganisms involved in the biofuel production will be provided and discussed to give and in-depth overview of the state of the art with broad spectrum of microorganisms and biofuels. For the sustainability of green fuel technologies, the book will also address biosafety of different production technologies and, social and political interest in promoting green fuels. These facts make this book very valuable for biofuels companies and scientific community\"-- Provided by publisher.
Book
Industrial energy efficiency assessment and prioritization model - An approach based on multi-criteria method PROMETHEE
In today’s scenario of increasing energy prices, new legislations, and rising consumer concerns regarding environmental issues, industries face an unprecedented challenge of reducing their energy consumption without negatively impacting their profit and productivity. Based on this, companies are focusing on analyzing their energy efficiency, which has various criteria to be considered, and at least three organizational levels. To close this gap, this study developed an Industrial energy efficiency assessment and prioritization model based on energy assessment literature. It utilized multi-criteria analysis for the prioritization of industrial energy efficiency measures. To achieve the goal, a literature review was conducted to map relevant energy efficiency practices from which an industrial energy efficiency assessment tool was developed through the lens of three organizational levels (plant, process, and machine). Subsequently, an energy-efficiency project prioritization tool was proposed using the multi-criteria PROMETHEE II method. The assessment and prioritization model was applied to an energy industry for refinement. It generated an overview of the company's energy efficiency maturity and a ranking of the most recommended measures for the optimal use of energy resources according to established criteria and their weights. Four subcategories (lighting, HVAC systems, compressed air, and motors) were analyzed for the organizational levels, and lighting presented the higher result of a maturity of 2.77 on a scale from 0 to 3, also the maturity of the company was 2.01, which means that is still space for improvement. The improvements were highlighted according to each subcategory studied, pointing to actions that needed to be developed to improve energy efficiency.
Journal Article
Nanomaterials for photocatalytic chemistry
\"This book concentrates on the emerging area of the utilization of (solar) photon energy for catalyzing useful chemical reactions (also called artificial photosynthesis) including water splitting, CO2 reduction, selective epoxidation, selective alcohol oxidation, coupling reactions, etc. The chapters in this book cover topics ranging from materials design at nanometer scale to nanomaterials synthesis to photocatalytically chemical conversion. This book can serve as a useful reference for those new to this field of research or already engaged in it, from graduate students to postdoctoral fellows and practicing researchers\"-- Provided by publisher.
Book
Hybrid BRR–MLP–CatBoost model with interpretable approaches for predicting industrial energy consumption: a case study of South Africa, Egypt, and Morocco
The industrial sector is the cornerstone of the global economy and remains the largest consumer of energy, accounting for 30.4% of global energy use in 2022. As nations pursue energy efficiency and sustainability goals, data-driven modeling approaches are increasingly essential to support strategic planning. This study aims to predict total final energy consumption in the industrial sectors of South Africa, Egypt, and Morocco by developing a hybrid machine learning framework based on data from 2000 to 2022, sourced from the International Energy Agency, the World Bank, and Our World in Data. The proposed framework integrates Bayesian ridge regression, multi-layer perceptron, and CatBoost to harness their complementary strengths, incorporating a comprehensive set of energy, demographic, and economic indicators. Performance evaluation using R2, MAPE, rRMSE, and MAE with fivefold cross-validation confirms the model’s high predictive accuracy. The hybrid model achieved an R2 of 0.992, with comparatively low error margins. To enhance interpretability, eXplainable Artificial Intelligence (XAI) techniques were applied. SHAP results reveal that fuel consumption is the most significant driver of industrial energy demand. Diverse counterfactual explanations (DICE) show that changes in renewable energy consumption and GDP cause the largest shifts in predicted demand. These insights guide smarter energy efficiency strategies.
Journal Article
Applications of nature-inspired computing in renewable energy systems
\"This book discusses the latest research on nature-inspired computing approaches applied to the design and development of renewable energy systems and provides new solutions to the renewable energy domain such as microgrids, wind power, and artificial neural networks\"-- Provided by publisher.
Book
Enabling Reliable Industrial Energy Savings Verification Through Hybrid Factored Conditional Restricted Boltzmann Machine and Generative Adversarial Network
Reliable quantification of industrial energy savings requires accurate detection of non-routine events (NREs) that distort post-retrofit baselines. Conventional statistical and rule-based anomaly detection methods often misinterpret operational variability, leading to biased or overstated savings under the International Performance Measurement and Verification Protocol (IPMVP). This study develops a novel IPMVP-compliant hybrid deep learning framework that integrates a deterministic Deep Neural Network (DNN) for baseline modeling with stochastic architectures, namely the Factored Conditional Restricted Boltzmann Machine (FCRBM) and Generative Adversarial Network (GAN), to capture probabilistic reconstruction patterns. Their outputs are fused using a hybrid thresholding mechanism that balances detection sensitivity and specificity. Using high-resolution data from an industrial glove manufacturing facility, the hybrid DNN–FCRBM model achieved the best trade-off, demonstrating an accuracy of 94.3%, a precision of 91.1%, and a low false positive rate of 5.1%. This model validated 11.32% industrial energy savings (approximately 478,050 kWh), equivalent to 237 tonnes of CO[sub.2] avoided. The integration of stochastic generative learning within a deterministic framework strengthens transparency, auditability, and IPMVP compliance, offering a scalable pathway for credible industrial energy savings verification.
Journal Article
The path to sustained growth : England's transition from an organic economy to an industrial revolution
\"Before the industrial revolution prolonged economic growth was unachievable. All economies were organic, dependent on plant photosynthesis to provide food, raw materials, and energy. This was true both of heat energy, derived from burning wood, and mechanical energy provided chiefly by human and animal muscle. The flow of energy from the sun captured by plant photosynthesis was the basis of all production and consumption. Britain began to escape the old restrictions by making increasing use of the vast stock of energy contained in coal measures, initially as a source of heat energy but eventually also of mechanical energy, thus making possible the industrial revolution. In this concise and accessible account of change between the reigns of Elizabeth I and Victoria, Wrigley describes how during this period Britain moved from the economic periphery of Europe to becoming briefly the world's leading economy, forging a path rapidly emulated by its competitors\"-- Provided by publisher.
Book
European Industrial Energy Intensity
We investigate the direct role of technological innovation and other factors influencing industrial energy intensity across 17 EU countries over 1995–2009. We develop an innovative industry-level patent dataset and find compelling evidence that patent stock negatively influences industrial energy intensity. In particular, we find a much stronger effect of patent stock on energy-intensive industries with an estimated coefficient of –0.138 which almost double that of less energy-intensive industries (estimated at –0.085). While our results show that energy price remains the major determinant of energy intensity, the chemicals industry, which is not covered by the EU Emissions Trading Scheme (ETS) during the sample period, appears more susceptible to energy prices relative to other energy-intensive industries that are covered by the EU ETS. Exploring regional differences in carbon taxation, we find a significant decline in energy intensity in Northern Europe owing to the carbon tax policy implemented in the early 1990s across the Nordic countries.
Journal Article
Residential and Industrial Energy Efficiency Improvements: A Dynamic General Equilibrium Analysis of the Rebound Effect
The aim of this paper is to investigate bi-directional spillovers into residential and industrial sectors induced by energy efficiency improvement (EEI) in both the short- and long-term, and the impact of nesting structure as well as the size of elasticities of substitution of production and utility functions on the magnitude and the transitional dynamic of rebound effect.
Developing a dynamic general equilibrium model, we demonstrate that residential EEIs spillover into the industrial sector through the labor supply channel and industrial EEIs spillover into the residential sector through the conventional income channel. Numerical simulations calibrated on the U.S. suggest that not taking into account these spillover effects could lead to misestimating the rebound effect notably of residential sector EEIs. We also demonstrate how the size and the duration of the rebound effect depend on the elasticities of substitution’s values. Numerical simulations suggest that alternative sets of value for the elasticities of substitution may give different sizable patterns of rebound effects in both the short- and long-term.
In policy terms, our results support the idea that energy efficiency policies should be implemented simultaneously with rebound effect offsetting policies by considering short- and long-term economy feedbacks. As a consequence, they require considering debates about what type of policy pathways are more effective in mitigating the rebound effect.
Journal Article