Integrated Control of Emission Reductions, Energy-Saving, and Cost–Benefit Using a Multi-Objective Optimization Technique in the Pulp and Paper Industry

Abstract

Reduction of water pollutant emissions and energy consumption is regarded as a key environmental objective for the pulp and paper industry. The paper develops a bottom-up model called the Industrial Water Pollutant Control and Technology Policy (IWPCTP) based on an industrial technology simulation system and multiconstraint technological optimization. Five policy scenarios covering the business as usual (BAU) scenario, the structural adjustment (SA) scenario, the cleaner technology promotion (CT) scenario, the end-treatment of pollutants (EOP) scenario, and the coupling measures (CM) scenario have been set to describe future policy measures related to the development of the pulp and paper industry from 2010-2020. The outcome of this study indicates that the energy saving amount under the CT scenario is the largest, while that under the SA scenario is the smallest. Under the CT scenario, savings by 2020 include 70 kt/year of chemical oxygen demand (COD) emission reductions and savings of 7443 kt of standard coal, 539.7 ton/year of ammonia nitrogen (NH4-N) emission reductions, and savings of 7444 kt of standard coal. Taking emission reductions, energy savings, and cost-benefit into consideration, cleaner technologies like highly efficient pulp washing, dry and wet feedstock preparation, and horizontal continuous cooking, medium and high consistency pulping and wood dry feedstock preparation are recommended.

Saving energy in China’s industry with a focus on electricity: a review of opportunities, potentials and environmental benefits

“…Kong et al (2017) estimated the energy-saving potentials of 23 available EETs in 2010. Research on future energy savings at the national level is limited (Lin & Moubarak, 2014a;Wen et al, 2015b;Zhou et al, 2011). Lin and Moubarak (2014a) established relationships between economic variables to forecast the energysaving potentials up to 2025.…”

“…Lin and Moubarak (2014a) established relationships between economic variables to forecast the energysaving potentials up to 2025. Wen et al (2015b) evaluated the co-benefits of 14 EETs on energy savings and water-pollution reduction from 2010 to 2020 by industrial water pollutant control and technology policy (IWPCTP) model. These studies indicate that efforts to improve energy efficiency will benefit both the economy and the environment by reducing energy costs and emissions of water pollutants and GHG.…”

Determinants of Electricity Demand in Nonmetallic Mineral Products Industry: Evidence from a Comparative Study of Japan and China

“…A per-unit-production figure (electricity consumption per unit of physical production) is of great significance for describing future technology policy measures [3,4] as well as obtaining meaning implications. For instance, bottom-up models have been adopted to simulate energy systems based on technologies for energy consumption and production, which could examine the physical reality of energy saving potential or the potential for carbon emissions mitigation [5,20,80].…”

“…During the period 1999-2010, the average electricity intensity of the Japanese non-metallic mineral products industry was 0.1619 kWh/CNY. Comparatively, the figure was 0.9468 kWh/CNY in the Chinese non-metallic mineral products industry (A per-unit-production figure (electricity consumption per unit of physical production) is of great significance for describing future technology policy measures [3,4] as well as obtaining meaning implications. For instance, bottom-up models have been adopted to simulate energy systems based on technologies for energy consumption and production, which could examine the physical reality of energy saving potential or the potential for carbon emissions mitigation [5].…”

An improved procedure to implement NSGA-III in coordinate waste management for urban agglomeration

“…Meanwhile, the application of multi-objective genetic algorithms (MOGAs) in the field of environmental research, most of which utilize the second-generation genetic algorithm (NSGAII) (Barak et al, 2016; Hu et al, 2005; Mousavi-Avval et al, 2017), has gained wide attention in recent years (Wen et al, 2015). The NSGAII algorithm has many advantages over other methods.…”